Browse Topic: Reaction and response times

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Research on Unmanned Aerial Vehicle Fault Detection Method Based on 1DCNN-BiLSTM2026-99-18667/17/2026
In recent years, drone technology has seen widespread application in both civilian and military fields. By 2025, China will introduce supportive policies from multiple dimensions, including industrial development, technological innovation, and application promotion, to significantly increase the number of UAVs in use and their frequency. However, drones are prone to malfunctions due to factors such as bad weather and electromagnetic interference, which may result in serious consequences, including property damage and casualties. Therefore, improving the accuracy of fault detection and the response time of drones is of great significance. Although current research has made progress, there are still deficiencies: First, most of them rely on a single or limited data source, resulting in incomplete information and vulnerability to interference, which leads to low detection accuracy and reliability; Second, traditional methods are mostly based on fixed thresholds or simple rules, lacking real-time dynamic monitoring and adaptive analysis capabilities, making it difficult to issue timely warnings of potential faults. To this end, this study proposes a multi-scale time series prediction model based on multimodal and multi-branch, integrating multimodal data, constructing a dual-branch architecture, and combining deep learning and attention mechanisms to enhance the anomaly detection effect of unmanned aerial vehicles. A dual-branch anomaly detection model based on 1DCNN-BiLSTM and continuous wavelet transform is proposed, including a trajectory prediction difference branch and a full time series data branch. In the dual-branch output stage, the attention gating mechanism is utilized to fuse features and improve the detection performance. The experimental results show that this model performs excellently in both normal trajectory prediction and anomaly detection, providing an effective solution for drone anomaly detection.
Pu, Zhenglin, Zhang, Lin
Passive Thermal Management of Battery Disconnect Units for Fast-Charging Electric Vehicles: A Comparative Study of Phase Change Material Heat Transfer Configurations2026-01-07597/1/2026
The widespread adoption of electric vehicles is currently hindered by long charging durations and limited infrastructure. While fast-charging technologies address these issues, they impose significant thermal loads on high-voltage components. Within this architecture, the Battery Disconnect Unit plays a critical role as it monitors and controls the connection between the battery, powertrain, and charging system. However, the high currents required for fast-charging often drive these units' temperatures beyond safe operating limits, necessitating advanced thermal solutions that do not require extensive redesigns of the vehicle's electrical layout. To address this challenge, this study proposes a passive thermal management solution using Phase Change Material heat transfer devices to enhance the thermal robustness of the component. The methodology employs a dual approach involving initial experimental testing to pinpoint specific thermal hotspots under high-power conditions, followed by detailed numerical simulations using GT-Power software to predict system behavior. Furthermore, the paper provides a comparative analysis of various configurations, assessing their impact on temperature reduction, response time, and thermal uniformity. The results demonstrate that appropriately designed passive solutions significantly improve thermal performance, effectively enabling higher charging power capabilities while minimizing system complexity and integration effort. This innovation provides a scalable and efficient path for improving overall vehicle performance and safety during rapid energy transfer events.
Salameh, Georges, Goumy, Guillaume, Frecinaux, Anthony, Ratajczack, Christelle, Palluel, Marlène, Noiseau, Pascal, Lardeux, Sébastien
Non-Intrusive Fatigue Detection for Pilots2026-26-07936/1/2026
Pilot fatigue represents a critical concern in aviation safety, as it can significantly impair cognitive functions, decision-making abilities, and reaction times. In addition to decreasing performance, in-flight chronic fatigue has negative long-term health effects. Possible causes of fatigue include sleep loss, extended time awake, circadian phase irregularities and workload. Conventionally, the risk due to fatigue in aerospace is reduced by flight time limits and controlled rest requirements. Despite regulations limiting flight time and enabling optimal rostering, fatigue cannot be prevented completely. Hence, there is need to detect pilot fatigue in real time. There is ongoing research to detect pilot fatigue using devices that can capture Electroencephalogram (EEG) and Electrocardiogram (ECG). Though these devices have high fidelity, they are intrusive and can limit pilot activity. This limitation could potentially be overcome by non-intrusive devices such as a smart watch/wrist band/goggles which can measure physiological parameters that provide insights into pilot’s mental health. Heart rate variability (HRV) is one such physiological marker of interest for detecting pilot fatigue in real time. HRV can be effectively derived by processing raw Photoplethysmography (PPG) signals to gain insights into the autonomic nervous system, enabling the assessment of physiological state. Wearable devices such as a wristwatch are used in the current study to measure PPG data. Time and frequency domain analysis were performed to evaluate the potential of HRV indices. The analysis of R-R intervals and the Low Frequency / High Frequency (LF/HF) ratio plots, derived from HRV signals, revealed distinct characteristics that differentiate between an alert and a fatigued pilot. This study demonstrates a reliable non-intrusive method for detecting pilot fatigue and enhancing flight safety.
Nyamagoudar, Vinayak, P R, Namratha, Ramachandran, Venkataramani
An Investigation of Drowsy Lane Departures from Driving Simulator Studies09-14-01-00145/4/2026
NHTSA is conducting research to evaluate the current state-of-the-art technology for lane departure warning (LDW) and lane-keeping assistance (LKA) technology. NHTSA is undertaking research to understand the nature of real-world lane departures and recovery behaviors. While some information about lane departures can be learned from crash datasets, the purpose of this work was to mine simulator datasets for lane departures, analyze them in greater detail than is possible from crash reports or naturalistic studies, and link their characteristics to driver drowsiness. The objective of the study was to determine whether there are differences in lane departure characteristics as a function of driver drowsiness. This research used a novel approach by combining data from six different driving simulator studies on driver drowsiness. The dataset included a sample of 380 drivers. Study drives occurred during overnight hours after periods of sleep deprivation, with participants being awake for at least 16 h prior to driving. Study drives ranged in duration from relatively short 45-min to nearly 4 h. The datasets were reduced to characterize 5805 individual lane departures. Lane departures were delineated into three phases (pre-departure, departure, and recovery) and two transition points (onset and reentry) to capture driver behaviors under drowsiness. We hypothesized that lane departures would look different under different levels of drowsiness. Drives took place across a range of roadway environments that included interstate highways, rural highways, rural roads, and low-speed urban areas. Drowsiness was sampled at points before, during, and after the drive using self-ratings [Karolinska Sleepiness Scale (KSS) or Stanford Sleepiness Scale (SSS)] as well as the expert Observational Rating of Drowsiness (ORD). High levels of drowsiness were associated with a narrow speed range at highway speeds and the least amount of throttle input, while low levels of drowsiness had more steering activity, more throttle input, and a broader range of speeds. The results of this study will improve understanding of vehicle kinematics and driver behavior in drowsy lane departures using a safe methodology to help address crash dataset limitations.
Schwarz, Chris, Gaspar, John, Shull, Emily, Venegas, Michael
Understanding and Modeling Effectiveness of Predictive Risk Notifications for Early Assistance for Safe Driving: A Public Road Testing Study09-14-01-00265/4/2026
While an enlarged lead time from risk notifications to collisions is widely acknowledged to facilitate safe driving, it remains challenging to effectively notify drivers of invisible risks and non-apparent risks coming from uncertain behaviors on the part of road users. The current study examined whether verbal notifications are able to assist early awareness of predictive risks. We also attempted to identify human and environmental factors that could possibly improve the effectiveness of predictive risk information. Twenty-eight licensed drivers participated in a public road test conducted in two different urban areas on 3 days. They drove predefined courses on which potential risk locations were identified prior to the test, using a sport utility vehicle equipped with an automatic verbal notification system triggered based on the distance to the potential risk locations. After passing through the locations each time, the participants were instructed to verbally evaluate the shift in awareness provided by the notification and the usefulness of the assistance. After the driving test was completed, we acquired a subjective evaluation on annoyance acceptability and a self-report of participants’ road usage frequency at notified locations in daily life, as well as questionnaires on their driving style and workload sensitivity. We found that the effectiveness of verbal notifications increased by conveying uncertainty risks at visible locations and by using interrogative sentences or expressions of risk target perspective, although it decreased as a function of age. Our model showed strong performance in predicting positive ratings for the notifications, but this was not the case for negative ratings. We identified individual characteristics and the risk factor of uncertainty as important features in our model. In conclusion, the findings provide an important reference for understanding the early notification of predictive risk and constructing a numerical model for the implementation of assistance systems in vehicles and nomadic devices.
Maruyama, Masaki, Koyama, Keiichiro, Ezaki, Toru, Sakamoto, Junichi, Sawada, Yuta, Matsuoka, Takahiro
Driver Response Times to Turn-In-Path Events from Real World Responses in SHRP-2 Data2026-01-05454/7/2026
This study analyzed driver behavior in Turn-In-Path (TIP) scenarios using the Second Strategic Highway Research Program (SHRP2) naturalistic driving dataset. A total of 167 real-world incidents, including both crashes and near-crashes, were examined to evaluate human driver perception-response times (PRT) and avoidance behaviors when an intruding vehicle (the principal other vehicle, or POV) turns into the path of a straight-moving subject vehicle (SV). The combined analysis includes TIP events involving POVs turning from intersecting roads to either cross or merge into the SV’s lane and continues in the direction of the SV. Each event was reviewed to identify the driver behavior in an emergency response event, with measurements taken from video and telematics data. Response time was measured across two different starting points. Key variables included time to conflict, POV behavior, SV driver engagement in secondary tasks, and environmental factors such as lighting and roadway geometry. Across both datasets, shorter time to contact was consistently associated with quicker driver responses. Driver responses were slower when the POV entered from the right as well as when drivers were engaged in visual-manual secondary tasks. In contrast, driver age and gender were not found to significantly affect PRT. This combined study expands the understanding of real-world driver response behavior in TIP scenarios and provides an empirical foundation for refining crash avoidance systems and modeling human performance in traffic conflict situations.
Dinakar, Swaroop, Muttart, Jeffrey, Maloney, Timothy, Adhikari, Bikram
Electric Vehicles & the Grid - Synergy or Strain?2026-01-03984/7/2026
As the demand for electrical power has surged over recent years due to the increasing popularity of data centers for Artificial Intelligence (AI) and Electric Vehicles (EVs), it is becoming evident that the aging electrical grid infrastructure is struggling to keep up. Some of the problems this aging infrastructure has resulted in include frequent blackouts due to weather related events, reduced efficiency resulting in higher maintenance costs and outdated communication systems causing poor monitoring and response times. Modernization of the grid in conjunction with integration of the transportation sector with the grid is essential to ensure the reliability and resiliency of the grid. Electric vehicles have dramatically increased in popularity, with most vehicle manufacturers offering at least one electric option in their lineups. Looking at recent developments in vehicle-to-grid (V2G) technology, a new possibility becomes evident; instead of straining the power grid, the electric vehicle can synergize with it. This becomes possible when EVs can facilitate charging during off-peak (low demand) hours and supplying power back to the grid during on-peak (high demand) hours. There are quite a few challenges associated with this approach, lack of standardized charging infrastructure and higher install costs, regulatory and policy hurdles, gaps in technological know-how particularly in relation to impact of power supplied by EVs on grid and effect of V2G on EV battery degradation in the long run, to name a few. This paper reviews the current power demand and supply along with existing and projected power consumption metrics. We also discuss the V2G strategy to effectively manage load requirements, incentives that can be provided to facilitate the execution, and the challenges associated with its widespread implementation. Finally, we discuss case studies of vehicles that incorporate V2G capability and their implications.
Dahlmann, Alexander Drake, Lele, Sneha
Virtual Sensor Development for Real-Time Estimation of Transient NOx Emissions for Internal Combustion Engine2026-01-03704/7/2026
Accurately measuring NOx emissions under transient engine conditions is becoming increasingly important with upcoming Euro 7 and EPA 2027 regulations. Traditional physical sensors often struggle with cost and response time, especially with aging of sensors in dynamic operation. This paper introduces a machine-learning–based virtual NOx sensor that can provide real-time emission estimates while reducing reliance on hardware sensors. The approach uses multiple machine-learning methods (Random Forest, Bootstrap Aggregating, Adaptive Boosting, Gradient Boosting, Extreme Gradient Boosting) and selected best one to establish correlations between engine operating parameters, measured steady-state data, and transient duty cycle NOx emissions. Validation across different duty cycles has shown strong alignment with physical sensor readings, with R2 values above 99.95% for training cycle data sets and above 95.34% for held-out cycles during training. The model needs to be trained with larger training samples to further improve accuracy for unseen data sets. By reducing sensor costs, this solution supports scalable use in production engines. The NOx virtual sensor can also serve as a redundancy measure to back up physical sensors, reducing the risk of compliance failures in case of physical sensor faults. Overall, the proposed method offers a cost-effective pathway to improve compliance monitoring, engine performance optimization, and regulatory readiness for the next generation of efficient powertrains.
Kumar, Chandan, Dahodwala, Mufaddel, Thawrani, Kiran
Benchmarking Driver Responses in Head-On Conflicts: Expanding the Framework for TTC, Affordances, and Uncertainty2026-01-05344/7/2026
Head-on emergency events present unique challenges for evaluating both human and automated-vehicle (AV) performance because they do not conform to a direct stimulus–response sequence. Instead, driver behavior in these scenarios follows a stimulus–wait–response pattern governed by time-to-conflict (TTC), uncertainty, and environmental affordances. Prior research has often failed to distinguish between conflict types, resulting in generalized reaction-time assumptions that do not account for contextual uncertainty. This study integrates simulator and naturalistic driving data from a four-part research program to establish objective benchmarks for driver responses in head-on encounters. When an encroaching vehicle crossed the centerline 2.5 s before impact, drivers initiated braking with a weighted average of approximately 1.0 s before impact. When the encroaching vehicle crossed or was first observed at approximately 3.5 s before impact, braking typically began with a weighted average of 1.3 s before impact, consistent with a deliberate waiting period rather than an immediate reaction. Across conditions, response variability increased with TTC, with the standard deviation scaling at 0.50 times the mean. In events where the encroaching driver corrected back to the proper lane, delayed responses were associated with successful avoidance. Steering behavior was influenced by roadside affordances, drivers steered right when no right-side obstacle was present but rarely steered right when any obstacle existed and drivers were likely to steer left when right-side obstacles were present. These findings reinforce the wait-and-see principle and provide empirically grounded benchmarks for evaluating human and AV responses in head-on emergency scenarios.
Muttart, Jeffrey, Dinakar, Swaroop, Maloney, Timothy, Adikhari, Bikram, Gernhard-Macha, Suntasty
Collision Avoidance Effectiveness of an Automated Driving System Using a Human Driver Behavior Reference Model in Reconstructed Fatal Collisions2026-01-05204/7/2026
Avoiding and mitigating any potential collision is dependent on (1) road user ability to avoid entering into a conflict (conflict avoidance effect) and (2) road user response should a conflict be entered (collision avoidance effect). This study examined the collision avoidance effect of the Waymo Driver, a currently deployed SAE level 4 automated driving system (ADS), using a human behavior reference model, designed to be representative of a human driver that is non-impaired, with eyes on the conflict (NIEON). Reliable performance benchmarking methodologies for assessing ADS performance are an essential component of determining system readiness. This consistently performing, always-attentive driver does not exist in the human population. Counterfactual simulations were run on responder collision scenarios based on reconstructions from a 10-year period of human fatal crashes from the Operational Design Domain of the Waymo ADS in Chandler, Arizona. Of 16 simulated conflicts entered, 12 (75%) were prevented by the Waymo Driver, and 10 (62.5%) were prevented by the NIEON model. The NIEON Model mitigated an additional 5 collisions and did not mitigate 1 collision. In these 16 conflicts entered, 93% of serious injury risk was reduced by the Waymo Driver, whereas 84% of serious injury risk was reduced by the NIEON model. Further, in a case-by-case evaluation, the Waymo Driver’s collision avoidance led to reduced serious injury risk when compared to the NIEON model in every simulated event. The results of this paper demonstrate that a reference model like NIEON can be used to benchmark ADS responder performance in response to high-risk initiating behaviors performed by the current driving population.
Scanlon, John M., Kusano, Kristofer D., Engstrom, Johan, Victor, Trent
Virtual Validation of ADAS Algorithms Using Driver-in-the-Loop Simulation2026-26-00501/16/2026
Driver-in-the-Loop (DIL) simulators have become crucial tools across automotive, aerospace, and maritime industries in enabling the evaluation of design concepts, testing of critical scenarios and provision of effective training in virtual environments. With the diverse applications of DIL simulators highlighting their significance in vehicle dynamics assessment, Advanced Driver Assistance Systems (ADAS) and autonomous vehicle development, testing of complex control systems is crucial for vehicle safety. By examining the current landscape of DIL simulator use cases, this paper critically focuses on Virtual Validation of ADAS algorithms by testing of repeatable scenarios and effect on driver response time through virtual stimuli of acoustic and optical warnings generated during simulation. To receive appropriate feedback from the driver, industrial grade actuators were integrated with a real-time controller, a high-performance workstation and simulation software called Virtual Test Drive (VTD). By developing an integrated solution for acquiring driver response, creation of scenarios and evaluation of control systems, this paper focuses on virtual validation of systems in a time saving and cost-effective manner.
Sharma, Chinmaya, Bhagat, Ajinkya, Kale, Jyoti Ganesh, Karle, Ujjwala
Transforming Manufacturing: Increasing Throughput and Reducing Production Complexity with Digital Twins2026-26-04491/16/2026
This paper presents a bidirectional digital twin developed for the Fischertechnik Smart Factory Kit, enabling real-time simulation and validation of production line modifications prior to actual deployment. The digital twin integrates with a Siemens Programmable Logic Controller (PLC) to mirror real-world operations, capturing live production data and visualizing key factory parameters, such as product, process, and resource metrics within a 3D environment. Engineers can test various optimization scenarios by adjusting robot speed and path, conveyor speeds, part & process sequences, and modifying equipment layout sizes to enhance efficiency. Based on the optimization scenarios, the best-performing configurations are identified using metrics such as throughput, cycle time, and resource utilization. Once validated, these changes are directly deployed to the PLC, ensuring seamless implementation. Beyond capacity optimization, this solution enhances overall production efficiency by minimizing idle time and parts waiting time, balancing workloads, and reducing unplanned disruptions. Additionally, by virtually simulating product variations and process changes, the digital twin helps identify design simplifications, reduce product complexity, and streamline manufacturing workflows. A digital twin of the manufacturing system serves as an integrated solution, unifying capabilities such as predictive maintenance, efficiency monitoring, simulation, and analytics in real time. By bridging technology gaps and offering a comprehensive view of the entire production process, it enhances decision-making, maximizes resource utilization, and facilitates seamless technology adoption across the factory. This approach significantly reduces downtime, accelerates response times, and boosts automation, demonstrating the transformative potential of digital twins in optimizing manufacturing operations [1].
Kumar, Rahul, Singh, Randhir
A Review on ‘Sensor Technologies’, its Application, Regulatory Standards & Emerging Trend Towards Safe Hydrogen Economy2026-26-01111/16/2026
In a developing country like India, the growing energy demand across all sectors underscores the urgent need for clean, sustainable, and efficient energy alternatives. Hydrogen stands out as a promising fuel, offering virtually zero emissions and helping to reduce greenhouse gas (GHG) emissions, which directly contributes to mitigating global warming, ensuring a cleaner environment, and lowering dependency on fossil fuels. In line with Sustainable Development Goal 7 (SDG 7), which seeks to guarantee that everyone has access to modern, cheap, and sustainable energy, hydrogen is well-positioned to be a major player in India's energy transformation. However, hydrogen has unique properties such as its wide flammability range, high reactivity, and high energy content present significant challenges in terms of safety, particularly in its storage, transportation, and usage. Improper handling or inadequate safety measures can lead to hazardous incidents, making robust testing, certification, and infrastructure development is vital for its safe deployment. Technology for hydrogen detection is essential for maintaining safety and adhering to legal standards. However, detecting hydrogen leaks poses significant challenges due to its unique physical properties: colourless, odourless, and tasteless, no smoke or visible trail, low density and high buoyancy etc. This paper reviews the current literature on hydrogen safety, with a focus on detection technologies, leakage prevention, and key considerations essential for the safe application of hydrogen in accordance with regulatory requirements. The paper discusses various sensor technologies and their underlying detection principles, including Catalytic, Resistance, Thermal conduction, Electrochemical, Work Function, Mechanical, Optical, Acoustic etc. Each sensor type is assessed for sensitivity, response time, selectivity, detection range, and suitability for different applications. This review aims to support researchers, industry stakeholders, and policymakers in identifying effective detection solutions and enhancing hydrogen safety frameworks for widespread adoption.
Pawar, Yuvraj, Dekate, Ajay Dinkar, Thipse, S, Belavadi Venkataramaiah, Shamsundara
Vehicle-to-Vehicle Negotiation Via Manoeuvre Coordination Messages for Cooperative Cut-in Scenarios2026-26-02741/16/2026
The automotive industry is rapidly extending the capabilities of automated systems by incorporating connectivity and cooperation features that enable real-time information exchange between vehicles and road infrastructure. Within the Connected, Cooperative, and Automated Mobility (CCAM) framework, Vehicle-to-Vehicle (V2V) communication is expected to play a key role in improving road safety, traffic efficiency, and driving comfort. This work addresses a practical implementation of the standardized Manoeuvre Coordination Messages (MCMs), as defined in the ongoing ETSI standard (ETSI TS 103 561). The proposed approach is demonstrated through a cooperative cut-in use case in which two vehicles negotiate a lane change manoeuvre. In the considered scenario, the ego vehicle, driven by a Highway Pilot (HWP) system, receives the intention to cut-in from a neighbouring cooperative vehicle through an MCM. In response, the ego vehicle adapts its behaviour by decelerating to generate a safe longitudinal gap, which allows the cooperative vehicle to merge the ego’s lane. The negotiation process relies on the bidirectional exchange of MCMs to coordinate the timing and trajectories, ensuring both vehicles complete the manoeuvre safely. Additionally, the Cooperative Awareness Messages (CAMs) allow the vehicles to share real-time information such as position, speed and heading. This connected-enhanced approach extends the capabilities of local perception systems, enabling an improved performance and reaction time to surround traffic participants. The described use case is implemented and validated in a prototype vehicle equipped with V2V communication capabilities and a Highway Pilot (HWP) SAE level 3 driving automation system. Proving ground tests demonstrate that the system can successfully negotiate cut-in manoeuvres in real time, enhancing both safety and traffic flow. The results confirm the feasibility of deploying standardized V2V coordination mechanisms within operational automated driving functions and lay the groundwork for broader integration into future CCAM applications.
Leiva Ricart, Gisela, Domingo Mateu, Bernat
Systemic Modelling and Simulation Approach to Estimate Brake Response Time in a Pneumatic Brake Systems2026-26-04691/16/2026
Brake response time in truck air brake systems is crucial for ensuring safety and operational efficiency. This paper details the development of a simulation model aimed at fulfilling all regulatory requirements for brake response time, as well as serving as a tool for stopping distance calculations. The actual pneumatic circuit, including brake valves, relay valves, brake chambers, and plumbing have been replicated. The aim is to use 1D simulations to predict the response time compliance during the pressurizing phase (when brakes are applied) of the brake system. A mathematical model is developed using a commercially available 1D simulation tool. This model employs a lumped parameter approach for the pneumatic components, with governing equations derived from compressible flow theory and empirical valve flow characteristics. The simulation outcomes provide detailed response time and pressure build-up profiles. Validation against 201 vehicle test cases showed 96% of simulations within ±10% of measured response times, aligning with FMVSS thresholds. These results confirm the model’s predictive accuracy and its value in optimizing brake system design while reducing physical testing. Overall, the proposed approach offers actionable insights to optimize air brake designs during the development phase, thereby, significantly reducing the need for expensive testing.
Kumbar, Praful, Murugesan, Karthik, Shannon, Rick
Optimizing Collision Warnings: A Shift from Time to Collide to Time to Brake2026-26-00471/16/2026
In automotive safety systems, Time to Collide (TTC) is traditionally used to trigger warnings in auto-emergency braking systems. However, TTC can lead to premature or inaccurate warnings as it is calculated based on the relative speed and distance between the ego and an obstacle. TTC does not consider the vehicle’s braking dynamics, such as brake prefill lag which varies across different vehicles, maximum deceleration, and the effectiveness of braking systems and assumes constant speed which may not always be realistic. We propose Time to Brake (TTB) as a more effective parameter for driver warnings. TTB directly relates to the action a driver needs to take—braking. It provides a clear indication of when braking should begin to avoid a collision, whereas TTC only tells us about the possibility of a collision. To calculate TTB we utilize the brake profile, which incorporates both deceleration and system jerk for improved accuracy. The proposed warning time is the sum of variable brake prefill lag, average driver reaction time, and TTB. TTB is calculated for two distinct scenarios due to differing constraints using Newtonian equations of motion. The comoving and oncoming scenario involve both ego and object colliding at the same location, but in the former, the relative velocity is zero, and in the latter the ego’s velocity is zero at the point of collision. This approach enhances driver response and safety by providing timely and relevant warnings. Tailored for specific braking dynamics, TTB improves the effectiveness of automotive safety systems.
Singh, Ashutosh Prakash, Kumawat, Himanshu, Gupta, Sara
Predicting Cut-in Events Using LSTM Based Techniques2026-26-06361/16/2026
Road accidents involving cut-in and sudden brake events on highways present major challenges to driver safety, often outpacing the response time of traditional Advanced Driver Assistance Systems (ADAS). The objective of this study is to predict potential collisions caused by cut-ins before ADAS intervention becomes necessary, allowing for earlier driver alerts and enhanced vehicle response. The proposed method employs machine learning and deep learning approaches, specifically Long Short-Term Memory (LSTM) networks, to forecast collision risks 0.5 to 3 seconds in advance. Synthetic data generation techniques are used to create rare but critical cut-in and braking scenarios, complementing real-world data from test vehicles and accident records. Key predictive features monitored include relative velocity, lateral velocity, and lane overlap, which provide dynamic indicators of imminent risk. Results show that the system achieves an average early warning time of 1.35 seconds in 40.206% of evaluated hazardous scenarios, significantly improving the chance for evasive maneuvers and collision avoidance. Compared to conventional reactive systems, our approach proactively identifies threats by integrating real-time sensing with predictive modeling. The conclusion drawn from this research is that combining synthetic event generation with LSTM-based predictive analytics can substantially enhance ADAS capabilities, reduce accident rates, and pave the way for smarter, more anticipatory vehicle safety systems. These findings offer an important advancement toward more intelligent road safety technologies that emphasize prevention rather than reaction.
Srivastava, Rohan, Nayak, Apoorva S., Suvvari, Sai Dileep, Satwik, Rahul, Bhattacharya, Abhinov
Research on Civil Aviation Transportation Scheduling Optimisation System Based on Artificial Intelligence2025-99-023312/23/2025
The rapid development of civil aviation industry makes it difficult for traditional flight scheduling methods to cope with the increasingly complex air transport demand. In this study, an AI-based civil aviation transportation scheduling optimisation system is designed, integrating a novel deep reinforcement learning framework with a validated multimodal fusion algorithm (MMFA) to address spatiotemporal dependencies in aviation data to construct the core architecture of the system. Measurement results show that the system effectively reduces the average flight delay time by 58.1%, improves the slot utilisation rate by 21.3%, increases the flight punctuality rate to 93.7%, and shortens the response time to emergencies by 62.5%. The high performance and significant economic benefits demonstrated by the system in the real environment provide a feasible solution for the intelligent upgrading of civil aviation transport.
Li, Mohan
Equivalent Circuit for Driving Piezoelectric Injectors2025-36-017712/18/2025
The activation of the fuel injector affects both engine performance and pollutant emissions. However, the automotive industry restricts access to information regarding the circuits and control strategies used in its vehicles. One way to optimize fuel injections is using piezoelectric injectors. These injectors utilize crystals that expand or contract when subjected to an electric current, moving the injector needle. They offer a response time up to four times faster than solenoid-type injectors and allow for multiple injections per combustion cycle. These characteristics result in higher combustion efficiency, reduced emissions, and lower noise levels, making piezoelectric injectors widely used in next-generation engines, where stricter emission and efficiency standards are required. This study aims to design a drive circuit for piezoelectric injectors in a common rail system, intended for use in a diesel injector test bench. Experimental measurement of voltage was obtained from an injector coupled to a running diesel engine. The developed equivalent circuit demonstrated the capability to drive piezoelectric injectors with voltage values close to those observed in a commercial injector installed in a diesel engine, validating its suitability for research and experimental applications. Additionally, injector operating curves were generated, evaluating the injected diesel mass flow rate for different energization times and injection pressure. The designed equivalent circuit successfully enabled the correct operation of piezoelectric injectors on the test bench, reproducing the expected charge and discharge behavior required for precise actuation.
Moreira, Vinicius Guerra, Silveira, Hairton Júnior José da, Morais Hanriot, Sérgio de, Euzébio, Wagner Roberto
Performance Evaluation of Linear Model-Based Predictive Control for Path Tracking in Light Autonomous Vehicles2025-36-000112/18/2025
This article focuses on the control of autonomous vehicles (AVs) using advanced methodologies, with particular emphasis on Model-based Predictive Control (MPC) as a tool for optimizing trajectory replication. The primary objective is to demonstrate that MPC can effectively minimize costs and improve efficiency in urban traffic scenarios. The study explores control strategies centered on reducing energy consumption and response time. Given the extensive research on this topic, the article evaluates and compares various control methods, including Pole Allocation, Linear Quadratic Regulator (LQR), and MPC, highlighting the superior capabilities of MPC in ensuring stability and adaptability. Simulations conducted in MATLAB are utilized to validate these approaches, focusing on maintaining trajectory stability during variations in the steering angle.
Baldi, Eduardo, Conrado, Guilherme Barreto Rollemberg, Ribeiro, Levy Pereira, Rodrigues, Gustavo Simão, Lopes, Elias Dias Rossi
Research on the Application of Flexible Pressure Sensor Array in Electric Vehicle Seats2025-99-033812/17/2025
In this study, an intelligent monitoring system for electric vehicle seats based on flexible pressure sensor array is proposed. Through the design of multi-layer composite film structure and the collaborative development of STM32 embedded platform, high-precision sensing (error<5%) and rapid response (<200ms) of pressure distribution are realized. The experimental results show that the linearity of the sensor array is ± 1.5% FS in the range of 0-100kpa, and the dynamic response time is 3.6 times higher than that of the traditional sensor; By establishing a three-level adjustment algorithm (fuzzy PID+LSTM prediction+genetic optimization), the seat comfort is improved by 20.5%, and the system energy consumption is reduced by 33.5%. The research provides theoretical and technical support for the transformation of intelligent seats from “passive support” to “active interaction”.
Huang, Yifeng, Rong, Daozhi, Lin, Guoyong, Huang, Zhengui, Wang, Ruliang, Tao, Chengxi
UAV Station Deployment for Emergency Traffic Incident Management: A Pareto Frontier-Based Approach2025-99-045012/10/2025
This study examines the issue of frequent traffic accidents leading to congestion and subsequent accidents. Timely investigation and management of these incidents is essential for effectively addressing this problem. This study aims to utilize Unmanned Aerial Vehicle (UAV) technology to improve the efficiency of assessing and investigating traffic accidents. We propose a bi-objective spatial optimization model based on identifying high-risk accident locations. This model combines coverage and median objectives within a service area, taking into account coverage requirements and optimizing site distribution. We also propose a constraint-based process to generate a Pareto frontier to help identify various alternative UAV station location scenarios. The model was validated using real traffic accident data from Nanning City, resulting in a UAV station configuration solution that reduces accident response time and improves assessment efficiency by considering multi-objective trade-offs. This study demonstrates the potential of UAV technology to improve the management and response to traffic accidents.
Li, Qiuling, Wan, Qian, Liu, Qianqian, Sun, Ke
Research on Ship Network Security Situation Awareness and Defense Mechanism Based on Improved Spatiotemporal Attention and Deep Reinforcement Learning2025-99-012611/11/2025
With the development of ship intelligence, network security threats are increasing day by day. This paper proposes a ship network security situation awareness algorithm based on an improved spatiotemporal attention mechanism, and constructs a supporting defense mechanism. The algorithm accurately captures changes in network security situation through dynamic weight allocation and multi-scale feature extraction. In the experimental simulation, OMNeT++ is combined with SUMO to build a ship network simulation environment, and Maritime - CPS - Dataset and other data sets are used for testing. The algorithm in this paper is compared with ARIMA, LSTM, GRU and other algorithms. The results show that in terms of situation awareness accuracy, the algorithm in this paper reaches 95.6%, which is 27.8% higher than ARIMA, 12.3% higher than LSTM, and 10.1% higher than GRU respectively; the average response time of the defense mechanism is shortened to 2.3 seconds, which is 40% faster than the traditional static defense strategy, the attack loss is reduced by 78%, and the resource occupancy rate is reduced by 35%. The experimental data fully verifies that the algorithm and defense mechanism significantly improve the ship network security protection capability, providing reliable technical support for the intelligent and safe operation of ships.
Kong, Zeyu, Zhou, Bofei, Wan, Shiyao
Automating Fault Detection Time Interval in Automotive Functional Safety Using HIL Simulation2025-28-027411/6/2025
Functional safety is driven by number of standards like in automotive its driven by ISO26262, in Aerospace its driven by DO-178C, and in Medical its driven by IEC 60601. Automotive electronic controllers must adhere to state-of-the-art functional safety standard provided by ISO26262. A critical functional safety requirement is the Fault Handling Time Interval (FHTI), which includes the Fault Detection Time Interval (FDTI) and Fault Reaction Time Interval (FRTI). The requirements for FHTI are derived from Failure Mode Effect Analysis (FMEA) conducted at the system level. Various fault categories are analyzed, including electrical faults (e.g., short to battery, short to ground, open circuits), systemic faults (e.g., sensor value stuck, sensor value beyond range), and communication faults (e.g., incorrect CAN message signal values). Controllers employ strategies such as debouncing and fault time maturity to detect these faults. Numerous FDTI requirements must be verified to ensure compliance with FMEA-identified faults. Significant portion of total quantum of Test procedures of entire system are fault injection test cases, Manual testing of these cases is cumbersome, hence automating these tests is crucial for efficient regression testing. In HIL environment, ECU variables and communication signals are available for processing within tool which contains fault information which needs to be processed for FDTI calculations. The paper examines diverse strategies to handle the complexity of FDTI test cases in the HIL environment through automation, leveraging tool features, time trigger, time synchronization, post-processing techniques and real-time calculations during test execution to process FDTI calculations, ensuring thorough verification of functional safety requirements.
Lengare, Sunil, Yadav, Vikaskumar, Shiraskar, Pallavi
IHX Technology and Its Effectiveness in Automotive AC Performance2025-28-043010/30/2025
The Internal Heat Exchanger (IHX) is an important component in modern car air conditioning (AC) systems, particularly in AC lines. It increases cooling efficiency by transferring heat from the high-pressure liquid refrigerant to the low-pressure vapor. By using this technology, refrigerant sub-cooling and superheating improve, resulting in higher cooling performance, lower energy usage, and less strain on the compressor. It improves vehicle fuel economy and a longer lifespan of AC components. Also, IHX prevents liquid refrigerant from entering the compressor, reducing the danger of damage and increasing system reliability. This optimization helps to maintain consistent refrigerant flow, reduces energy consumption, and improves the overall Coefficient of Performance (COP). The implementation of an IHX technology in AC lines results in more compact, streamlined system designs, which allow for better temperature management, faster response times, and lower cooling loads. An IHX can boost cooling capacity and efficiency in AC lines by 10-15% in comparison to normal AC lines without an IHX. It reduces weight and space needs by making the system more compact. IHX is a useful solution for the automotive industry`s AC lines since it makes installation and maintenance easy. As a result, the adoption of an IHX in AC lines is a key innovation for boosting the performance, reliability, and sustainability of air conditioning systems, contributing to energy efficiency and reduced environmental impact.
Dudeja, Kailash, Singh, Saniya
Automatic Brake Failure Detection with Electromagnetic Braking System2025-28-037310/30/2025
Brake failures in the vehicles can cause hazardous accidents so having a better monitoring and emergency braking system is very important. So, this project consists of an autonomous brake failure detector integrated with Automatic Braking using Electromagnetic coil braking which detects the braking failure at the time and applied the combinations of the brakes, to overcome this kind of accidents. So, here the system comprises of IR sensor circuit, control unit and electromagnetic braking system. How it works: The IR sensor monitors the brake wire, and if the wire is broken, the control unit activates the electromagnetic brakes, stopping the vehicle in a safe manner. This system enhances vehicle safety by ensuring immediate braking action without driver intervention. Key advantages include real-time brake monitoring, reduced mechanical wear, quick response time, and an automatic failsafe mechanism. The system’s minimal reliance on hydraulic components also makes it suitable for harsh or variable conditions. The proposed system can be widely implemented in automobiles, especially those using drum brakes, as well as railway systems to prevent accidents due to brake failure. Future advancements in predictive maintenance, machine learning, and AI integration could further improve the reliability, adaptability, and overall efficiency of this advanced braking system.
Raja, Selvakumar, John, Godwin, Siddarth, J P, Senthilkumar, Akash, Mathew, Abhay, R. S., Nakandhrakumar, Nandagopal, Sasikumar, Arumugam, Sivasankar
Review of Bio-Inspired Actuators and Their Potential for Adaptive Vehicle Control12-09-02-001110/10/2025
Adaptive vehicle control systems are crucial for enhancing safety, performance, and efficiency in modern transportation, particularly as vehicles become increasingly automated and responsive to dynamic environments. This review explores the advancements in bio-inspired actuators and their potential applications in adaptive vehicle control systems. Bio-inspired actuators, which mimic natural mechanisms such as muscle movement and plant tropism, offer unique advantages such as flexibility, adaptability, and energy efficiency. The article categorizes these actuators based on their mechanisms, including shape memory alloys, dielectric elastomers, ionic polymer–metal composites, and soft pneumatic actuators. The review highlights the properties, operating principles, technical maturity, and potential applications for each mechanism in automotive systems. Additionally, it investigates current uses of these actuators in adaptive suspension, active steering, braking systems, and human–machine interfaces for autonomous vehicles. The review further outlines the advantages of bio-inspired actuators, including their energy efficiency and adaptability to road conditions, while addressing key challenges such as material limitations, response times, and integration with existing automotive control systems. Finally, the article discusses future directions, including the integration of bio-inspired actuators with machine learning and advancements in material science, to enable more efficient and responsive adaptive vehicle control systems. This review concludes that bio-inspired actuators will play a significant role in the future of the automotive industry, offering several advantages related to weight, power, flexibility, and cost when compared to conventional systems.
Mittal, Vikram, Shah, Rajesh, Roshan, Mathew
From Temperature to Wear: A Quantitative Analysis of Tire Influence on Vehicle Cornering Using Handling Key Performance Indicators2025-01-50609/16/2025
Vehicle behavior is strongly influenced by tire performance, as tires serve as the primary interface between the vehicle and the road surface. Since identical vehicles equipped with different tire sets—or even the same tires operating under varying thermal and wear conditions—can exhibit significantly different handling characteristics, this study aims to quantify their impact on both steady-state and transient cornering responses through a dedicated evaluation methodology. To demonstrate the generalization of the proposed approach, three completely different validated vehicle digital twins—a passenger car, a sports car, and a formula car—are analyzed in a virtual environment, employing Vi-Car Real Time for vehicle and scenario representations, and RIDEsuite for tire modeling, considering thermal and wear effects. The simulations were designed using a structured design of experiments approach, resulting in 15 predefined combinations of tire temperature and wear states. Results show that operating outside the tire’s optimal thermal and wear conditions significantly affects vehicle handling balance, responsiveness, and driver perception of agility. These effects scale with tire performance level: while standard passenger car tires exhibit limited sensitivity, slick formula tires show substantial variations in grip and cornering stiffness, reaching deviations of approximately 10% and 35% from their nominal values, respectively. Vehicle steady-state analyses indicate that front axle wear increases understeer, while rear axle wear reduces overall stability—resulting, for example, in a 25% increase in peak sideslip angle in the sports car configuration. Transient analyses further confirm that temperature has a more pronounced effect than wear, particularly on yaw rate and lateral acceleration response times, with variations reaching up to ±10% relative to optimal thermal conditions. This work highlights the need to include tire condition effects in handling target definition and validation processes, recommending careful monitoring of tire states during standardized ISO maneuvers. Fixed metrics should be replaced by performance ranges that reflect actual tire operating states, whether for custom-developed or off-the-shelf tires.
Aratri, Roberto, Romagnuolo, Fabio, De Pinto, Stefano, Farroni, Flavio, De Bellis, Sergio, Bottiglione, Francesco, Mantriota, Giacomo, Sakhnevych, Aleksandr
Preliminary Multi-Physics Modelling and Characterization of a Magneto-Rheological Brake2025-01-03479/15/2025
Magneto-Rheological Fluid (MRF) is a smart material used in several applications for its ability to switch from fluid behaviour to solid-like conditions if a magnetic field is present. The dependency of viscosity on magnetic field makes this fluid suitable for braking system of electric vehicles, thanks to its high controllability and response time in the whole operative range. The main parameters that influence the behaviour of the fluid, and so the braking action of the system, are magnetic field and rotational velocity. In general, the variable physical properties make it complicated to simulate the system and its behaviour in different operating conditions. Therefore, it is usually necessary to build a physical prototype to experimentally verify the response of the braking system at different driving conditions. This paper presents the development of a virtual model of Magneto-Rheological Brakes (MRB) whose validity is extended to different driving conditions. This can be accomplished by creating two coupled model, an electro-magnetic and a fluid-dynamic, using respectively Ansys Electronics Desktop 2D Maxwell and Ansys Fluent. Both the models are validated by comparing the magnetic flux density and the braking torque obtained from the experimental test campaign of the braking system prototype at different coil currents. The simulation and experimental results present a good correlation and allow to evaluate a wide range of operative and driving conditions of the braking system. The validation allows to use the developed simulation methodology to design and to adapt the braking system to any other specific application.
De Luca, Elena, Imberti, Giovanni, de Carvalho Pinheiro, Henrique, Carello, Massimiliana
LightNetDehaze: Lightweight and Unsupervised Single-Image Dense Haze Removal12-09-01-00055/26/2025
Image dehazing techniques can play a vital role in object detection, surveillance, and accident prevention, especially in scenarios where visibility is compromised because of light scattering by atmospheric particles. To obtain a high-quality image or as an initial step in processing, it’s crucial to restore the scene’s information from a single image, given that this is an ill-posed inverse problem. The present approach utilized an unsupervised learning approach to predict the transmission map from a hazy image and used YOLOv8n to detect the car from a clear recovered image. The dehazing model utilized a lightweight parallel channel architecture to extract features from the input image and estimate the transmission map. The clear image is recovered using an atmospheric scattering model and given to the YOLOv8n for car detection. By incorporating dark channel prior loss during training, the model eliminates the need for a paired dataset. The proposed dehazing model with fewer parameters speeds up the dehazing process, which can detect the objects in less response time. The network follows unsupervised learning, which eliminates the need of ground truth image or transmission map of a clear image. The proposed method tried to solve the issue of high computational complexity and long latency when used as a preprocessing stage in computer vision applications. The proposed network ranks first in terms of parameters and FLOPs, which are lower by scale 102 and 103, respectively, compared to the method ranked second. The results highlight the effectiveness of the proposed method compared to other methods and ranked first in number of car detections using YOLOv8n. The inference time to dehaze the image is comparable to the method ranked first and 66% lower than the third rank.
Dave, Chintan, Patel, Hetal, Kumar, Ahlad
Human-AI Interaction in Autonomous Medical Evacuation HelicoptersF-0081-2025-02825/20/2025
The vertical flight industry is on its way to a transformative era, with autonomous technologies set to alter aerial vehicle operations. While it seems certain that fully autonomous helicopters will eventually be deployed for a variety of missions, some high-stakes situations—like medical evacuations (MEDEVAC)—will for the foreseeable future demand human participation in the form of Emergency Medical Care-giving Crew. This study describes the testbed built to run and investigate hypothetical future situations in which a helicopter is autonomously piloted while a human medic with no aviation training, subjected to aviation and medical emergencies, manages patient care onboard. A total of 22 participants, with emergency medical technician certification, nursing or a medical board certification, were invited to run and evaluate the use of AI pilot (AP) in different scenarios of medical evacuation under the following emergencies: medical, empty fuel tank, pressure sensor miscalibration, and engine failure. A comprehensive evaluation of both objective and subjective performance metrics revealed that novice medical professionals could effectively execute medical evacuation operations in conjunction with an AI pilot, even during unforeseen circumstances. The analysis of response times unveiled distinct perspectives on how medics perceive and manage various emergency situations when an AP functions as a collaborative and effective team member.
Doda, Sanya, Feigh, Karen, Agbeyibor, Richard, Cortes, Carmen, Kolb, Jack, Magalhaes, Jose
Optimizing Comprehension and Performance in Technical Documentation: Does Adding Multimedia to Written Instructions Help to Safely and Accurately Perform Maintenance Tasks?F-0081-2025-01445/20/2025
As part of a human factors research project aimed at optimizing technical documentation used in helicopter maintenance with multimedia elements, we compared different instruction formats to observe their effects on the performance of an assembly task. This task offers us the opportunity to test procedures that call for similar actions as a maintenance task (e.g., localization, action sequencing, assembly). Static (i.e., image and image with text) and dynamic instruction formats (i.e., video, video with text and video with audio) were compared to determine if dynamic formats allowed a better motor performance of the task for assembly reaction time (time needed to complete the assembly) and accuracy. We were also interested in how the use of the text instructions interacted with both visual dynamic and static instructions. Reaction times were recorded and measured with eye tracking data. Subjective data was collected in questionnaires during and after the experiment. Results showed significant differences in the time spent on the instructions and the time spent on the assembly, depending on the format of instructions. Overall, assembly time is shorter with video instruction formats, but videos took longer to be consulted than static formats. Results also showed a difference in the number of actions required to do the assembly. Videos facilitated the right path of action sequence in comparison with static formats. With the analysis of both subjective and objective data, the results give us a better idea of the advantages and drawbacks of using dynamic formats in technical documentation.
Faye, Myriam, Jahchan, Nataly, Condamines, Anne, Amadieu, Franck
Variable Speed Limit Control Based on Deep Deterministic Policy Gradient under Mixed Traffic Environment12-09-01-00045/6/2025
The existing variable speed limit (VSL) control strategies rely on variable message signs, leading to slow response times and sensitivity to driver compliance. These methods struggle to adapt to environments where both connected automated vehicles (CAVs) and manual vehicles coexist. This article proposes a VSL control strategy using the deep deterministic policy gradient (DDPG) algorithm to optimize travel time, reduce collision risks, and minimize energy consumption. The algorithm leverages real-time traffic data and prior speed limits to generate new control actions. A reward function is designed within a DDPG-based actor-critic framework to determine optimal speed limits. The proposed strategy was tested in two scenarios and compared against no-control, rule-based control, and DDQN-based control methods. The simulation results indicate that the proposed control strategy outperforms existing approaches in terms of improving TTS (total time spent), enhancing the throughput efficiency of the bottleneck area, and reducing the spatial and temporal extent of traffic congestion. Compared to the suboptimal DDQN-based VSL control, the proposed strategy improves TTS by 9.3% in Scenario 1 and by 11% in Scenario 2. The sensitivity analysis shows that the proposed control strategy improves performance as the penetration rate of CAVs increases. However, when the penetration rate reaches a certain threshold, the potential for further optimization becomes limited. Furthermore, higher time-to-collision (TTC) values, influenced by the reward function r 2, enhance traffic safety.
Ding, Xibin, Zhang, Zhaolei, Liu, Zhizhen, Tang, Feng
Determining Worst-Case Execution Time Bounds for Multi-Core Processors2025-01-01555/2/2025
Demonstrating deadline adherence for real-time tasks is a common requirement in all safety norms. Timing verification has to address two levels: the code level (worst-case execution time) and the scheduling level (worst-case response time). Determining which methodology is suited best depends on the characteristics of the target processor. All contemporary microprocessors try to maximize the instruction-level parallelism by sophisticated performance-enhancing features that make the execution time of a particular instruction dependent on the execution history. On multi-core systems, the execution time additionally is influenced by interference effects on shared resources caused by concurrent activities on the different cores, which are not controlled by the scheduling algorithm. In the avionics domain, the new FAA AC 20-193 / EASA AMC 20-193 guidance documents formalize predictability aspects of multi-core systems and derive adequate measures for timing verification. Timing verification is a long standing and still very challenging topic. Established techniques include response time analysis, worst-case execution time analysis and real-time tracing. The goal of this article is to summarize the aspects relevant for timing verification, and give an overview of the available techniques. We also explicitly address multi-core considerations, focusing on the latest certification authorities’ publications from the avionics domain.
Kaestner, Daniel, Gebhard, Gernot, Huembert, Christian, Pister, Markus, Wegener, Simon, Ferdinand, Christian
Dynamics Modeling and Lateral Stability of Tank Transport Vehicles2025-01-80754/1/2025
In this paper, the equivalent elliptic gauge pendulum model of liquid sloshing in tank is established, the pendulum dynamic equation of tank in non-inertial frame of reference is derived, and the dynamics model of tank transporter is constructed by force analysis of the whole vehicle. A liquid tank car model was built in TruckSim to study its dynamic response characteristics. Aiming at the problem that the coupling effect between liquid sloshiness in tank and tank car can easily affect the rolling stability of vehicle, the roll dynamics model of tank heavy vehicle is established based on the parameterized equivalent elliptic gauge single pendulum model, and the influence of different lateral acceleration and suspension system on the roll stability is studied. The results show that the coupling effect between the motion state of the tank car and the liquid slosh lengthens the oscillation period of the liquid slosh in the tank, and the amplitude of the load transfer rate of the tank car increases with the increase of the lateral acceleration, thus reducing the stability of the vehicle. In addition, the study also pointed out that the liquid viscosity mainly affects the transient response of the vehicle, which is manifested in that the steady-state response time of the vehicle is shortened and the overshoot is reduced with the increase of the liquid viscosity, and this effect is more significant when the tank length axis ratio is increased. In summary, this study not only reveals the key mechanical characteristics of tank transporters during dynamic driving, but also provides optimization suggestions for different liquid filling ratios and tank shapes, providing an important reference for improving the stability and safety of tank vehicles.
Yukang, Guo
Assessing the Impact of Mirror Technology on Driver Perception and Safety: Traditional vs. Camera-Based Systems2025-01-86644/1/2025
Camera-based mirror systems (CBMS) are being adopted by commercial fleets based on the potential improvements to operational efficiency through improved aerodynamics, resulting in better fuel economy, improved maneuverability, and the potential improvement for overall safety. Until CBMS are widely adopted it will be expected that drivers will be required to adapt to both conventional glass mirrors and CBMS which could have potential impact on the safety and performance of the driver when moving between vehicles with and without CBMS. To understand the potential impact to driver perception and safety, along with other human factors related to CBMS, laboratory testing was performed to understand the impact of CBMS and conventional glass mirrors. Drivers were subjected to various, nominal driving scenarios using a truck equipped with conventional glass mirrors, CBMS, and both glass mirrors and CBMS, to observe the differences in metrics such as head and eye movement, reaction time, and perception of distance. The finds from this study will serve as the baseline measurements for future research regarding off-nominal driving scenarios and hardware failures of CBMS, as well as inform potential future policy regarding CBMS for the use in commercial vehicles in lieu of conventional glass mirrors.
Siekmann, Adam, Prikhodko, Vitaly, Sujan, Vivek
Operational Evaluation of a Pre-Production L2 System HMI2025-01-80504/1/2025
The study analyzed data from on-road drives with a pre-production Level 2 (L2) partial automation system using a sample of 27 drivers ranging from 21 to 75 years of age. The system provides continuous automatic lateral and longitudinal control but requires the driver to remain attentive and intervene when necessary. The L2 system was equipped with a Driving Monitoring System (DMS) that issued escalating alerts to remind the driver to pay attention or take over when needed. During the 14-month study period, drivers completed 354,768 miles of travel with the L2 system engaged, totaling 5,913 trips. The results of the study showed that drivers were highly responsive to attention reminders and takeover alerts, with high compliance rates and quick response times. Importantly, there was no evidence of habituation to these alerts over time. These findings support the effectiveness of the system's DMS and alert HMI (Human-Machine Interface) strategy in promoting the proper use of the system with increased usage and exposure.
Llaneras, Robert, Glaser, Yi, Green, Charles, August, Maureen, Landry, Steven
A Proactive System to Anticipate and Store the Damage Information on a Parked Car Using Machine Learning Algorithm2025-01-82114/1/2025
Vehicle ADAS Systems majorly comprises of two functions: Driving and Parking. The most common form of damage to the vehicle which goes unnoticed with unidentified cause are parking damages. A vehicle once parked at a certain location may get damaged without knowledge of the user. In this work developed a solution that not only pre-warns the driver but also prepares the vehicle beforehand if it suspects a damage may occur. This eliminates the latency between damage and information capture, detects small damages such as scratches, classifies the type of damage and informs the user beforehand. This is solution is different from our competitors as the existing solutions informs the user about the scratches/damages, but these solutions are expensive, have high response time, and the damage information is captured after the damage has occurred. The solution consists of the following check blocks: Precondition, Sensor Control and Action Module. The Precondition Module observes the vehicle parking location and GPS data to inform the driver about the parking area's accident history, enhancing pre-warning capabilities. It also blocks ambient noise using Active Band Pass filter along with Sliding Window FFT Algorithm for effective recording of vehicle damage noises. The Sensor Control block uses Ultrasonic and IMU sensors to sense presence of human being/ object within a certain threshold limit which is calculates a Risk Factor based on the Distance, Velocity and Acceleration of the approaching object. If this threshold limit is crossed, the vehicle opens its camera and microphone to start recording. The Action block then classifies the type of Damage Detected as a Minor or Major using sensor fusion techniques of IMU, Microphone and Camera Data. This is fed to the Robust CNN Machine Learning Algorithm which classifies the damage and extent and informs the user using proprietary application including images. This proactive approach offers significant improvements over existing solutions, providing a robust mechanism to protect parked vehicles from unnoticed damages.
Debnath, Sarnab, Patil, Prasad, Belur Subramanya, Sheshagiri, Govinda, Shiva Prasad
Leveraging Triggering Conditions for Efficient Scenario-Based Testing of Automated Vehicles12-08-04-00352/19/2025
Scenario-based testing has become a central approach of safety verification and validation (V&V) of automated driving. The standard ISO 21448: Safety of the intended functionality (SOTIF) [1] proposes triggering conditions (e.g., an occluded traffic sign) as a new aspect to be considered to organize scenario-based testing. In this contribution, we discuss the requirements and the strategy of testing triggering conditions in an iterative, SOTIF-oriented V&V process. Accordingly, we illustrate a method for generating test scenarios for evaluating potential triggering conditions. We apply the proposed method in a two-fold case study: We demonstrate how to derive test scenarios and test these with a virtual automated driving system in simulation. We provide an analysis of the testing result to show how triggering condition-based testing facilitates spotting the weakness of the system. Besides, we exhibit the applicability of the method based on multiple triggering conditions and nominal scenarios from an industrial context.
Zhu, Zhijing, Philipp, Robin, Howar, Falk
Deep Learning-Enhanced Direct Torque Control of BLDC Motors for Optimized Electric Vehicle Performance12-08-04-00362/12/2025
Electric vehicles (EVs) represent a promising solution to reduce environmental issues and decrease dependency on fossil fuels. The main drawback associated with the direct torque control (DTC) scheme is that it is incapable of improving the efficiency and response time of the EVs. To overcome this problem, integrating deep learning (DL) techniques into DTC offers a valuable solution to enhance the performance of the drive system of EVs. This article introduces three control methods to improve the output for DTC-based BLDC motor drives: a traditional proportional–integral for speed controller (speed PI), a neural network fitting (NNF)-based speed controller (speed NNF), and a custom neural (CN) network-based speed controller (speed CN). The NNF and CN are DL techniques designed to overcome the limitations of conventional PI controllers, such as retaining the percentage overshoot, settling times, and improving the system’s efficiency. The CN controller reduced the torque ripple by 15%, maintained the percentage overshoot by 10–15%, and also improved the settling time by 5%, leading to a 17.5% improvement in energy efficiency compared to the PI controller. The adaptive DL controller provides a 20% faster response time in regulating the torque output during dynamic driving conditions. DL-based DTC speed control improves the BLDC motor performance compared to the traditional PI controllers. The PI controller is simple and efficient for steady-state but shows poor performance in dynamic conditions due to large overshoot and long settling time. The NNF controller improves accuracy in static conditions. The CN controller offers better performance and dynamic flexibility with fast adaptation but requires higher computational power and is more complex to implement. The performance assessments of EVs are validated by developing the FTP72 and US06 driving cycle. This research appears to play a crucial role in advancing propulsion systems for EVs in the future.
Patel, Sandesh, Yadav, Shekhar, Tiwari, Nitesh
Design and Finite Element Analysis of Pneumatically Actuated Soft Gripper under Combined Positive and Negative Pressure2025-28-00442/7/2025
Soft-bending actuators have garnered significant interest in robotics and biomedical engineering due to their ability to mimic the bending motions of natural organisms. Using either positive or negative pressure, most soft pneumatic actuators for bending actuation have modified their design accordingly. In this study, we propose a novel soft bending actuator that utilizes combined positive and negative pressures to achieve enhanced performance and control. The actuator consists of a flexible elastomeric chamber divided into two compartments: a positive pressure chamber and a negative pressure chamber. Controlled bending motion can be achieved by selectively applying positive and negative pressures to the respective chambers. The combined positive and negative pressure allowed for faster response times and increased flexibility compared to traditional soft actuators. Because of its adaptability, controllability, and improved performance can be used for various jobs that call for careful handling or compliant environmental contact. The actuator's simple design and cost-effective manufacturing process contribute to its practicality and scalability. The modeling and conducting simulations on a soft robotic combined positive and negative pressure actuator also aim to design an adaptive soft-robotic gripper with reduced effort and investigate the up scaling of such grippers to extend their applicability to heavy payload handling and assembly. Once the results from simulations and experiments conducted by models are collaborated, the geometrical parameters are modified to get improved results. The improved model is compared in terms of pressure range, bending angle, versatility, and weight-carrying capacity. Simulation is done on Ansys for real-time results. The parametric study helps in establishing correlations between pressure and deflections to accurately control the motion of soft grippers
Lalson, Abirami, Sadique, Anwar
Optimization of Biodiesel Production from Tannery Industry Fleshing Wastes Using Response Surface Methodology2025-28-01152/7/2025
In response to rising emissions and pollutants, an alternative and environmentally friendly synthesis is gaining prominence on the energy sources. The leather industries generate substantial amount of waste and fleshing oil extracted from fleshing which is rich in lipids and presents a viable feedstock for biodiesel production. In this research work, Response Surface Methodology (RSM) is used to optimize the conversion of leather fleshing oil into biodiesel using three parameters such as operating temperature, reaction time, and molar ratio. Experiments were carried out to determine the most optimal conditions and the response on yield (%) and viscosity (mm2/s) based on a 17-run Box–Behnken Design matrix. Stochastic model parameters such as R2 (0.9715 and 0.9793), adjusted R2 (0.9349 and 0.9527), predicted R2 (0.8327 and 0.7656), and high F-values (26.52 and 36.78) of both responses (yield and viscosity) were found to be statistically significant and warranted model adequacy. ANOVA and regression analysis resulted in significant two-way interactions among variables relating to response. The optimal conditions were predicted at 61°C, 180 minutes of reaction time, and a molar ratio of 10:1 generated a yield of 92.901% with the viscosity of 3.629. Experimental trails were conducted at the predicted conditions and found a maximum yield of 90.52% with the minimum viscosity of 3.46. The predicted and experimental results were reported to be in close agreement.
P, Kanthasamy, Selvan, Arul Mozhi, P, Shanmugam
Research on the Intelligent Vehicle Trajectory Tracking Control Based on Optimal Preview Distance02-18-01-00051/3/2025
Accurate and responsive trajectory tracking is a critical challenge in intelligent vehicle control system. To improve the adaptability and real-time performance of intelligent vehicle trajectory tracking controllers, we propose a genetic algorithm adaptive preview (GAAP) scheme that offline optimizes the preview distance based on vehicle speed and reference path curvature. The goal is to obtain the optimal preview distance that balances tracking accuracy, stability, and real-time performance. By establishing a relationship between optimal preview distance, speed, and curvature, we enhance real-time performance through online table checking during trajectory tracking. Our trajectory tracking error model takes into account not only position errors but also heading errors. A feedback–feedforward trajectory tracking controller is then designed to achieve rapid responses without compromising robustness. Simulation tests conducted under straight circular arc condition and double lane change condition using CarSim/Simulink validate the effectiveness of our proposed scheme. Experimental results indicate that our proposed GAAP scheme improves real-time performance by approximately 86%, with a maximum response adjustment time of only 0.2 s, demonstrating significant advantages over existing schemes.
Cheng, Kehan, Zhang, Huanhuan, Hu, Shengli, Ning, Qianjia
Modeling and Simulation of an Electromagnetic Brake-by-Wire System for Formula SAE Vehicles2024-36-016912/20/2024
Electric vehicles represent a shift towards sustainability in the automotive industry, with the Brake-by-Wire (BBW) system as an innovation to enhance safety, and performance. This study proposes an electromagnetic BBW system for Formula SAE vehicles, optimizing an electromagnet with a genetic algorithm as the actuator. Through a selection process from a million individuals, the system was modeled. Integrated with electric motors using CarMaker® software, the optimized electromagnet surpassed the minimum required force of 228.08 N without reaching its nominal current of 12.5 A, achieving a force of 231.1 N for 150 W power, indicating an energy efficiency of 0.706 N/Watt. The system also exhibited a response time of 17.92ms for an 80 bar increase, 1.52 times better than compared systems. Simulation under varying braking intensities demonstrated dynamic behavior, with settling times for slow, moderate, and sharp braking at 193 ms, 62 ms, and 21 ms, respectively. Efficiency during different braking scenarios yielded energy recovery rates of 33.25%, 8.18%, and 5.34%, respectively. These results validate the proposed system.
Salgado, Vinícius Batista Alves, Gomes, Deilton Gonçalves, Andrade Lima, Cláudio
Trailer Disengage Confirmation Alarm System for Electric Subsystems2024-28-021312/5/2024
The truck industry's primary focus is on global transportation, necessitating the efficient movement of goods and materials. There are many types of trucks designed for different purposes, and one of the most significant ones is the tractor trailer which offers great flexibility and can carry heavy loads. The tractor-trailer assembly unit consists of a complex integration of mechanical, electrical, and pneumatic connections, each serving a critical role in the overall functionality and performance of the vehicle. The disconnection of electrical interconnections between the truck trailer and tractor is crucial to prevent damage to the connectors within the wiring harness, which can lead to hazardous situations on the road. The tractor unit serves as the power source, while the trailer is responsible for carrying cargo, with the wiring harness being a crucial yet vulnerable component. When the trailer disengages from the fifth wheel coupling, it is vital to ensure that the electrical connections, which control lighting and trailer brakes, are also properly disconnected to prevent damage and potential safety risks. The proposed system employs advanced sensor technologies and intelligent algorithms to continuously monitor the status of these electrical connections. In the event of a disconnection, the system activates a robust alarm mechanism to promptly notify the driver, thus mitigating the risk of accidents and ensuring the safety of both the vehicle and other road users. This paper presents a Trailer-Tractor Disengage Alarm System (TTDAS) designed to enhance safety in the electrical connections between the tractor and trailer of commercial vehicles. Key features of the TTDAS include real-time monitoring, rapid response times, and compatibility with various trailer configurations. The paper details the system architecture, encompassing the integration of sensors, control units, and the alarm mechanism. Additionally, the paper explores the algorithmic logic utilized to accurately detect trailer disengagements, thereby enhancing the reliability of the system.
Singh, Amandeep, Kumar, Pradeep, Suresh, Karthikrajan, Kotian, Pradeep, T, Thirunavukkarasu, Chitreddy, Bharath, R, Sunilkumar
ZF's latest safety advances open up, tighten up and DOUBLE DOWN24AUTP08_028/1/2024
ZF rethinks safety with new airbags, belt tensioner. ZF knows that the steering wheel remains one of the most relevant components in an automotive interior, because this is where drivers have direct contact to the vehicle. As steering wheels become adorned with more functions than some drivers know what to do with, ZF put Marc Schledorn in charge of the teams rethinking how the driver airbag could operate in a world with ever-busier steering wheels. The solution is a new type of steering wheel airbag that ZF Lifetec (ZF's renamed Passive Safety Systems division) announced in June. Instead of moving through a thermoplastic airbag cover mechanically fixed in the center of the wheel, Schledorn told SAE Media, the new design positions the airbag on the top side of the steering wheel and then expands through the upper rim of the wheel when needed.
Blanco, Sebastian
Test Cycle Simulation of Bench Based on Deep Reinforcement Learning15-17-03-00157/31/2024
Test cycle simulation is an essential part of the vehicle-in-the-loop test, and the deep reinforcement learning algorithm model is able to accurately control the drastic change of speed during the simulated vehicle driving process. In order to conduct a simulated cycle test of the vehicle, a vehicle model including driver, battery, motor, transmission system, and vehicle dynamics is established in MATLAB/Simulink. Additionally, a bench load simulation system based on the speed-tracking algorithm of the forward model is established. Taking the driver model action as input and the vehicle gas/brake pedal opening as the action space, the deep deterministic policy gradient (DDPG) algorithm is used to update the entire model. This process yields the dynamic response of the output end of the bench model, ultimately producing the optimal intelligent driver model to simulate the vehicle’s completion of the World Light Vehicle Test Cycle (WLTC) on the bench. The results indicate that the algorithm exhibits good convergence in the simulation, throughout the WLTC simulation, the driver always kept the vehicle speed error within 1 km/h, and the response time is less than 0.5 s under the vehicle’s starting condition. In comparison to the PID control algorithm and the model predictive control (MPC) algorithm, it demonstrates smaller speed error and response time, ensuring accuracy, high efficiency, and safety during the indoor vehicle-in-the-loop test.
Gong, Xiaohao, Li, Xu, Hu, Xiong, Li, Wenli
Firefighting Vehicle Dispatch and Route Planning Model Based on Priority Passage Rights12-08-02-00147/11/2024
Efficient fire rescue operations in urban environments are critical for saving lives and reducing property damage. By utilizing connected vehicle systems (CVS) for firefighting vehicles planning, we can reduce the response time to fires while lowering the operational costs of fire stations. This research presents an innovative nonlinear mixed-integer programming model to enhance fire rescue operations in urban settings. The model focuses on expediting the movement of firefighting vehicles within intricate traffic networks, effectively tackling the complexities associated with collaborative dispatch decisions and optimal path planning for multiple response units. This method is validated using a small-scale traffic network, providing foundational insights into parameter impacts. A case study in Sioux Falls shows its superiority over traditional “nearest dispatch” methods, optimizing both cost and response time significantly. Sensitivity analyses involving clearance speed, clearance time, minimum rescue force, and fire loss parameters contribute to the enhancement of urban fire rescue operations and the refinement of practical decision support systems.
Wei, Shibo, Gu, Yu, Liu, Han
Experimental Analysis of Force Recovery and Response Time Using Strain Measurement Sensors in Stress Wave Force Balance2024-26-04516/1/2024
Severe problem of aerodynamic heating and drag force are inherent with any hypersonic space vehicle like space shuttle, missiles etc. For proper design of vehicle, the drag force measurement become very crucial. Ground based test facilities are employed for these estimates along with any suitable force balance as well as sensors. There are many sensors (Accelerometer, Strain gauge and Piezofilm) reported in the literature that is used for evaluating the actual aerodynamic forces over test model in high speed flow. As per previous study, the piezofilm also become an alternative sensor over the strain gauges due to its simple instrumentation. For current investigation, the piezofilm and strain gauge sensors have mounted on same stress force balance to evaluate the response time as well as accuracy of predicted force at the same instant. However, these force balance need to be calibrated for inverse prediction of the force from recorded responses. A reliable multi point calibration methodology has been used to recover the calibration force. Initially, a blunt bicone shaped scaled “DASA CTV” model has been fabricated in house along with three component stress wave balance. Further, a multi-point calibration experiment has been performed over a test model at nine different locations. In literature, there is no any evidence has been found that effect of various sensors during calibration or actual shock tunnel experiments on a recovered forces. Hence, a calibration experimental studies focus on various sensors mounted on stress wave force balance to understand its behavior and also recovery of calibration forces during the experiment.
Kamal, Abhishek, Deka, Sushmita, Sahoo, Niranjan, Kulkarni, Vinayak
PRODUCT BRIEFS24AUTP06_136/1/2024
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