Browse Topic: Control systems

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Hybrid Physics-Informed Neural Network and PID Control for Fault-Tolerant Autonomous Multi-copters2026-26-0770To be published on 06/01/2026
This paper addresses the critical challenge of fault-tolerant control in autonomous multi-copters, particularly under conditions of one or two rotor failures a scenario that often leads to severe instability and a complete loss of directional control due to unbalanced torque and resultant autorotation. Existing advanced control strategies, including optimal approaches such as LQR, typically require precise system modeling and state estimation, which are difficult to achieve in real-world, dynamic failure scenarios. Alternative methods like fuzzy logic, sliding mode control, and gain-scheduling either lack robust generalization or are impractical for enumerating all possible failure cases. In this work, a hybrid control framework integrating Physics Informed Neural Networks (PINN) with a standard PID controller is proposed for fault-tolerant operation of autonomous multi-copters subject to multiple actuator failures. PINNs incorporate governing physical laws as regularization in their
Charapalle, Samruddhi, Venugopalan, Nandagopalan, Nerkundram Muralidharan, Arun, Sundararaj, Laveen
Electrical Harnessing in Satellite Launch Vehicles of ISRO – Methods for Sustainable, Safe, and Reliable systems2026-26-0779To be published on 06/01/2026
The electrical harness serves as the "neural network" for satellite launch vehicles, providing the interfaces for all guidance, health monitoring, and control systems during a mission. This system is composed of cables, and connectors. The wires are made of metallic conductors, typically copper plated with silver or gold to enhance conductivity and prevent oxidation. These conductors are insulated with polymeric materials such as PTFE, Kapton, ETFE, or TKT. Connectors, which are the interface points for electrical circuits, consist of gold or nickel-plated copper contacts housed within a shell. A typical launch vehicle contains an extensive network of electrical harnesses, with approximately 4,200 connectors and 110,000 meters of cable, which contributes to about 30% of the vehicle's total weight. The weight is mostly due to the polymeric insulation rather than the conductors. Challenges and Solutions for Sustainable Systems The paper highlights several key issues with current
K S, NITHISH, TR, Binny, D S, PRAVEEN KUMAR
Electric Health-Monitoring Wiring (E-Wiring) for Aircraft2026-26-0785To be published on 06/01/2026
Modern aircraft depend on extensive electrical wiring networks for power distribution, avionics, and control systems; however, these wiring systems are vulnerable to wear, insulation degradation, and arcing over time, leading to safety risks and costly unscheduled maintenance. The proposed Electric Health-Monitoring Wiring (E-Wiring) system integrates temperature, current, insulation, vibration, and environmental sensors directly into aircraft wiring harnesses to enable continuous monitoring and intelligent fault detection. Data from these embedded sensors are processed through a distributed edge AI network, forming an Electrical Health Monitoring System (EHMS) capable of real-time diagnostics, predictive maintenance, and fault localization. The architecture comprises smart cable segments with sensor nodes, local harness gateways for edge processing, aircraft-level EHMS integration via AFDX/Ethernet, and cockpit or maintenance displays linked to ground-based cloud analytics for fleet
Tammana, Bala Sai Sri Rohit, Murthy, Harsha, Mendu, HarikaSivaniSunandha
Automated health surveillance system for passenger2026-26-0794To be published on 06/01/2026
Ensuring passenger safety and comfort remains a top priority in the aviation industry, particularly during in-flight medical emergencies involving cardiac and respiratory complications. The absence of immediate medical intervention and the limitations of current aircraft health monitoring systems pose significant risks. Existing systems primarily depend on manual observation and basic sensors, lacking continuous health assessment, data analytics, and adaptive response mechanisms. This paper proposes a Smart Passenger Health Monitoring System that integrates Heart Rate (BPM), Electrocardiogram (ECG), and Peripheral Capillary Oxygen Saturation (SpO₂) monitoring into a single intelligent framework. The system utilizes high-precision biomedical sensors for continuous data acquisition, while an embedded microcontroller processes physiological parameters in real time to detect abnormalities. Upon identifying potential distress, the system triggers an automated seat adjustment mechanism that
S, Tejashree, D, Kousalya
AI Based Energy Sustained Series Hybrid Aircraft2026-26-0776To be published on 06/01/2026
This paper investigates the energy consumption characteristics of series hybrid aircraft with a focus on comparing conventional energy management approaches against an AI-powered optimization framework. The study comprehensively models the energy demands of a series hybrid aircraft across all major flight phases, including Idle & Ground Operations, Taxi, Takeoff, Climb, Cruise, Descent, Approach, Landing, and Rollout & Taxi. For each phase, detailed mathematical formulations are developed to capture power requirements and energy flow, incorporating real-time operational parameters to enhance the accuracy of the energy consumption estimations measured in kilowatt-hours (kWh). The AI-based optimization leverages advanced control strategies, specifically Model Predictive Control (MPC) and Reinforcement Learning (RL) algorithms, to dynamically manage the aircraft's energy systems. MPC is employed to predict and optimize future energy usage by solving constrained optimization problems over
Kanchagar, Amogha
A Latent Space Framework for Modeling Transient Engine Emissions Using Joint Embedding Predictive Architectures2026-01-0423To be published on 04/07/2026
Accurately modeling exhaust emissions during transient events, such as rapid acceleration and deceleration, remains a long-standing challenge in the automotive field and is a prerequisite for real-time control and optimization in modern powertrain systems. The nonlinear emission behavior is difficult to capture, and early mathematical models frequently relied on approximations that limited their accuracy. With the rise of data-driven methods, especially deep neural networks, including multilayer perceptrons and long short-term memory (LSTM) networks, modeling capabilities have improved significantly. However, to achieve meaningful accuracy, these models often require deep architectures, which increase computational load and inference time. This makes their application in real-time control systems challenging, as controllers must run at high sampling rates while maintaining accuracy, which is often a contradictory requirement. This paper addresses the trade-off with a new framework
Sundaram, Ganesh, Gehra, Tobias, Ulmen, Jonas, Heubaum, Mirjan, Görges, Daniel, Günthner, Michael
Sensor Fusion Method for In-Cabin Humidity Prediction2026-01-0131To be published on 04/07/2026
Maintaining optimal in-cabin humidity levels is part of occupant comfort, air quality, and the effective operation of climate control systems, particularly for functions like windshield defogging. This paper introduces a novel sensor fusion methodology for predicting in-cabin humidity distribution without dedicated humidity sensor. The proposed approach leverages readily available vehicle data, integrating information from ambient temperature sensors, in-cabin temperature sensors, occupant detection systems, window status, and climate control settings. By intelligently fusing these diverse data streams, a predictive model is developed to infer the dynamic humidity conditions within the vehicle cabin. We discuss the complex interactions between these parameters, such as the moisture contribution from occupants, the influence of external air ingress through open windows, and the dehumidifying or humidifying effects of the Heating, Ventilation, and Air Conditioning system. The paper
Ghannam, Mahmoud, Schroeter, Robert, Shaik, Faizan
Development of the Honda S+ Shift Utilizing e:HEV System2026-01-0418To be published on 04/07/2026
Honda is promoting mobility electrification to realize a carbon-neutral society by 2050. Hybrid vehicles will remain advantageous over electric vehicles in terms of manufacturing cost and driving range until renewable energy usage increases, charging infrastructure is sufficiently developed, and battery costs are reduced. In response to this situation, Honda has developed a new control system, “Honda S+ Shift”, which further enhances the “emotional value of driving pleasure” inherent to the e:HEV system and creates new value for hybrid vehicles. Honda S+ Shift synchronizes the engine and vehicle speed and selects a virtual gear position according to the driver's operation such as acceleration, cornering, and deceleration. Subsequently, the system achieves the required system output in cooperation with a dedicated energy management system. It also works with each vehicle system, such as drive force control, sound control, and meter cluster, to stimulate all five senses of the driver
Murata, Naoya, Narimoto, Ryosuke, Saito, Masatoshi, Ishida, Daichi, GUNJI, HIROKI, UKAI, YOHEI, Kurachi, Shinobu, SHIKI, KAZUKI, Nagakura, Akari, Maeda, Sadaharu, Mitogawa, Terumasa
Model-Based Design and dSPACE/MathWorks Environment Enable Innovative Engineering Education2026-01-0073To be published on 04/07/2026
The exponentially growing complexity of engineering systems, such as a robotic system, an autonomous vehicle, and an unmanned aerial vehicle, requires sophisticated control strategies that can efficiently coordinate the system’s operation in various environments. The traditional control design approaches present significant challenges for control engineers to keep up with the increasing complexity and changing requirements. To advance embedded control system design, a paradigm shift from traditional development approaches toward more structured, systematic methodologies that can manage the multi-domain nature of control systems is critically needed. Model-based design approach is emerging as a solution for this demand. Model-based design approach uses a system model for control system development, from requirements capture to control system design, implementation, and testing. It provides an integrated environment for design, implementation, automatic code generation, and validation
Repaka, Sindhura, Chen, Bo
Study of Approaches for Enhanced Traction Control Response for Hybrid Powertrain Architectures2026-01-0415To be published on 04/07/2026
Traction Control functionality requires fast and accurate response by powertrain system for wheel slip control. Close loop control effort of wheel slip shall be greatly reduced with more accurate feedforward torque. This paper explores the method of achieving requested traction control torque with better accuracy for torque converter-based hybrid electric powertrain architectures using improved dynamic modeling of torque converter. This method uses input inertia compensation involving feedforward and closed loop control modeling of torque converter and includes conversion of traction control intervention request through appropriate domains. Commanded torques by hybrid controller at wheel with and without these approaches are compared in Model in the loop simulation environment to identify optimum approach.
Karogal, Indrasen, Ober, Mitchell, Banuso, Abdulquadri, Sharma, Ashay
A Comparison of Road Grade Preview Signals from Lidar and Maps2026-01-0026To be published on 04/07/2026
Road grade can impact the energy efficiency, safety, and comfort associated with automated vehicle control systems. Currently, control systems that attempt to compensate for road grade are designed with one of two assumptions. Either the grade is only known once the vehicle is driving over the road segment through proprioception, or complete knowledge of the oncoming road grade is known from a pre-made map. Both assumptions limit the performance of a control system, as not having a preview signal prevents proactive grade compensation, whereas relying only on map data potentially subjects the control system to missing or outdated information. These limits can be avoided by measuring the oncoming grade in real-time using on-board lidar sensors. In this work, we use point returns accumulated during travel to estimate the grade at each waypoint along a path. The estimated grade is defined as the difference in height between the front and rear wheelbase at a given waypoint. Kalman filtering
Schexnaydre, Logan, Poovalappil, Aman, Robinette, Darrell, Bos, Jeremy
A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)2026-01-0147To be published on 04/07/2026
The shared autonomy framework has become an option with great potential in the field of autonomous vehicles. Human and machine control decisions typically demonstrate strengths in different scenarios. As a result, the robustness of systems can be enhanced by the collaboration between humans and autonomy. A shared autonomy architecture that takes into account both human and environmental factors was proposed in this work. The authority distribution between the human operator and the autonomy algorithm was determined by the Shared Autonomy Arbiter (SAB). Designed with a two-tier structure, the SAB incorporated a policy-level decision module, as well as a numerical-level arbitration tuning module. A fuzzy inference system (FIS) was incorporated to enhance the noise tolerance of the policy selection module. Furthermore, the human factor was taken into account by applying a projection to the users’ control input. The human operator’s control decision was projected by the Adaptive
Sang, I-Chen, Norris, William, Patterson, Albert, Sreenivas, Ramavarapu S., Soylemezoglu PhD, Ahmet, Nottage, Dustin S.
Backward Fuzzy Driving Control for 6×4 Off-Road Vehicles2026-01-0148To be published on 04/07/2026
Offroad autonomous vehicle systems must be able to operate across unstructured and variable terrain while avoiding obstacles. This presents significant challenges in vehicle and control system design, especially for less conventional platforms such as 6x4 vehicles. While forward driving autonomy has developed and matured in recent years, effective reverse navigation remains an under-explored area of vehicle co-design. Reversing 6x4 vehicles have limited rear steering authority, an extended wheelbase, and asymmetric traction, which introduce complex dynamics into any control system that is used. To address this need, a robust and experimentally validated fuzzy logic control architecture for 6x4 reverse navigation was developed during the course of this project. This architecture incorporates both near-field and long-range path data with adaptive outputs controlling steering and velocity based on a rule base that covers the whole vehicle state space. This method has low computational
Dekhterman, Sam, Patterson, Albert, Sreenivas PhD, Ramavarapu, Norris, William, Soylemezoglu PhD, Ahmet, Nottage PhD, Dustin
Multiple-Parameter Joint Estimation for Unmanned Mining Trucks Based on Real-Time Cornering Stiffness Identification2026-01-0618To be published on 04/07/2026
High-precision estimation of key vehicle–road state parameters is essential for accurate and safe vehicle control, as well as for reliable trajectory tracking and path planning. However, the strong nonlinearity of tire forces makes real-time estimation of tire cornering stiffness challenging, and the uncertainty of this parameter significantly limits the performance of conventional estimation methods. To overcome this issue, this study proposes a novel joint estimation framework that integrates the Square-Root Cubature Kalman Filter (SCKF) with Moving Horizon Estimation (MHE). In the proposed approach, the SCKF provides high-accuracy estimation of the vehicle sideslip angle, while the MHE enables real-time identification and updating of tire cornering stiffness using vehicle state information over a short horizon. This establishes a closed-loop joint estimation mechanism between sideslip angle and cornering stiffness. Furthermore, the real-time updated cornering stiffness is
Xia, Xue, Shen, Peihong, Jiao, Leqi, Li, Tao, Chen, Huiyong, Zhao, Kun, Jiao, Leqi, Zhao, Zhiguo
Vehicle Test Platform with Experimentally Correlated Tire Model for Torque Vectoring Control2026-01-0622To be published on 04/07/2026
This paper presents a testing platform for torque vectoring controls utilizing a 10 degree of freedom (DOF) vehicle simulation and a ⅕ scale radio control test vehicle. These vehicle simulations or “Digital Twins” have been widely adopted throughout the automotive industry for their lower operating costs and ease of implementation. Virtual models are not perfect representations of reality, however, and physical testing is still necessary to validate systems for use in the real world. This is especially true when testing safety-critical features such as Advanced Driver Assist Systems (ADAS). As a result, a simulation environment working in conjunction with a test vehicle represents an optimal hybrid approach. In this work, a 1/5 scale radio controlled vehicle with torque vectoring capability is designed and assembled. A high fidelity vehicle model is then constructed in the Matlab/Simulink environment to model test vehicle behavior. The test vehicle features independent suspension for
Petersen, Nicholas Conner, Robinette, Darrell
Efficient Embedded Control of Nonlinear Systems for Software-Defined Vehicles: A Case Study Using the Inverted Pendulum2026-01-0075To be published on 04/07/2026
Software-defined vehicles (SDVs) are reshaping automotive control architectures by shifting intelligence to embedded systems, where computational efficiency is paramount. This paper presents the implementation of multiple control strategies such as PID, LQR, and MPC for the inverted pendulum on a cart problem, a canonical example of nonlinear and unstable system control. The objective is to demonstrate how sophisticated control algorithms can be optimized for execution on microcontrollers with limited processing power and memory, reflecting the constraints typical of embedded platforms in SDVs. Each control strategy is implemented with careful consideration of algorithmic complexity, real-time responsiveness, and resource utilization. Performance is evaluated across key metrics including stability, convergence time, and computational load, enabling a comparative analysis that highlights trade-offs between control fidelity and hardware efficiency. The results provide actionable insights
Vupparige, Varun, Pandya, Vidit
Neural Surrogates and Reinforcement Learning for Heavy-Duty Diesel Torque Control: A Data-Driven Alternative to PID2026-01-0199To be published on 04/07/2026
Accurately reproducing the torque trace of regulatory drive cycles is central to heavy-duty diesel emissions certification and repeatable development testing. Conventional controllers—PI/PID with gain scheduling and feedforward mapping—can meet requirements but demand extensive hand-tuning and struggle with strong nonlinearities and time-varying delay. Building on prior work that quantified the limits and benefits of gain-scheduled and feedforward strategies under the FTP transient cycle, this work investigates a fully data-driven control framework. First, a recurrent neural network (LSTM) torque surrogate is trained on dynamometer data to capture engine dynamics from readily available signals (e.g., engine speed, commanded pedal/torque, recent torque history) and actuator constraints. Second, this learned “world model” is used to train a reinforcement learning (RL) policy that outputs pedal commands to minimize torque-tracking error while regularizing control effort and respecting
Cook, James, Puzinauskas, Paulius, Bittle, Joshua, Hall, Spencer
Adaptive Economic Model Predictive Control for Engine Emissions Management with Compensation for Performance Drift2026-01-0287To be published on 04/07/2026
This paper addresses the changes in engine emissions due to in-use component changes through the synergistic application of predictive control, machine learning, and onboard adaptation. In particular, we consider an adaptive economic Model Predictive Control (eMPC) strategy to mitigate the effects of performance drift on Nitrogen Oxides (NOx) and Soot emissions from compression ignition (diesel) engines. A performance drift block, which applies a multiplier and offset to nominal emissions, is integrated with a high-fidelity Neural Network (NN) plant model to simulate these characteristic changes. To counteract variability, two online adaptation methods are integrated within the eMPC framework: One is based on Recursive Least Squares (RLS) and another on a continuously updated online NN. The proposed control architecture is validated through simulations over standard transient cycles. Results demonstrate that while the rate-based eMPC possesses inherent robustness to performance drift
ZHANG, JIADI, Li, Xiao, Kolmanovsky, Ilya, Tsutsumi, Munecika, Nakada, Hayato
Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning2026-01-0041To be published on 04/07/2026
This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging Reinforcement Learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating
Zhang, Yaozhong, Ammourah, Rami, Han, Jihun, Moawad, Ayman, Shen, Daliang, Karbowski, Dominik
BEV drivetrain parameters sensitivity for efficient and effective active damping controls modeling2026-01-0004To be published on 04/07/2026
Effective active damping control is critical for Battery Electric Vehicle drive quality. Central to this control is open loop frequency response function between actuator (motor torque) and response (motor speed). While many driveline parameters influence the system dynamics, sensitivity analysis reveals that only a few of them significantly shape the resonance frequency and amplitude of crucial low frequency driveline modes. In this study, an orthogonal array-based sensitivity analysis is conducted and validated using 1D simulation tool as Amesim. The results show that identifying the most influential parameters enables meaningful model simplification without loss of accuracy, guiding quick and efficient modelling to help design control system. This approach provides engineers with a practical framework to focus on critical variables of driveline torsional system, reduce model complexity, and implement effective active damping controls strategies.
Sharma, Pranjal, Kolluri, Murali Mohan, Lin, Chihang
Intersection Assist For Vehicle With Dual-Trailers Using Nonlinear Model Predictive Control And Unscented Kalman Filter2026-01-0074To be published on 04/07/2026
This paper presents a novel approach utilizing Nonlinear Model Predictive Control (NMPC) and Unscented Kalman Filter (UKF) to predict system state and control the trajectory of the vehicle with double trailers in an intersection turn scenario. The UKF estimates vehicle and trailers’ lateral traversal velocity states and the NMPC controls the vehicle acceleration and steering to maintain the vehicle’s desired heading through the turn. The vehicle’s lateral traversal velocity function is formulated using Lyapunov based method which is used as a propagation function in the UKF to improve the estimation accuracy. The lateral traversal velocity is then used as one of the constraints in the NMPC problem. The overall estimation and the control scheme are formulated and assessed in the simulation environment. The simulation results show good tracking and curb avoidance performance.
Malla, Rijan
Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization2026-01-0162To be published on 04/07/2026
Building upon previous work that successfully employed a Reinforcement Learning (RL) agent for the autonomous optimization of transmission shift programs to enhance fuel efficiency , this paper addresses a critical limitation of that approach: the neglect of human-centric factors. While the prior methodology achieved substantial fuel consumption reductions by training an RL agent in a Software-in-the-Loop (SiL) environment, it did not explicitly account for aspects such as driver comfort and preferences, which are paramount for realworld user acceptance and drivability. This work presents a multi-objective optimization framework extending the artificial calibrator to simultaneously maximize fuel efficiency and enhance driver comfort. The method introduces a modified RL reward function that penalizes undesirable shift behavior to ensure a smooth driving experience (drivability). This new methodology also incorporates a mechanism to capture and integrate driver preferences, moving beyond
Kengne Dzegou, Thierry Junior, Schober, Florian, Rebesberger, Ron, Henze, Roman, Sturm, Axel
Method of Estimating Electric Vehicle Charge Time Based on Neural Networks2026-01-0382To be published on 04/07/2026
Accurate prediction of electric vehicle charging time is critically hindered by dynamic, non-linear factors including battery aging which is indicated by the State of Health (SOH), substantial power diversion to thermal management systems in extreme temperatures, fluctuating user-defined accessory loads, and hardware limitations of the charging infrastructure. Traditional estimation methods, reliant on static models or predefined calibrations, fail to adapt to these real-world variables, leading to inaccurate predictions and user dissatisfaction. This paper presents a novel data-driven estimation framework utilizing a tailored feedforward neural network architecture specifically designed for this complex task. The model processes a sensitive set of inputs—including initial State of Charge (SOC), SOH, battery temperature, charging station power level and user-selected target SOC—to effectively capture the intricate, non-linear interdependencies governing the charging process. The
Xie, Zhentao, shojaei, Sina, Weslati, Feisel
Embedded Real-Time Control of a Distributed Trailer Propulsion System with Regenerative Braking for Net Neutral Load Towing2026-01-0065To be published on 04/07/2026
Towing significantly degrades vehicle efficiency, reducing battery-electric vehicle (BEV) range by up to 50% and increasing internal-combustion engine (ICE) fuel consumption by ~30%. To address this challenge, this paper presents a real-time embedded control architecture for a distributed trailer propulsion system that actively regulates drawbar force to achieve net neutral load towing while enabling regenerative braking. Unlike prior e-trailer concepts, this platform demonstrates a cost-effective, fully networked, model-based control implementation using commercial off-the-shelf components. The control system integrates dual Raspberry Pi controllers running ROS 2: one samples a hitch-mounted load cell at 100 Hz and the other processes OBD-II/CAN messages. Controllers designed in MATLAB/Simulink are deployed to a 5 kWh LiFePO₄ pack and a 5 kW traction motor, utilising embedded Linux for real-time scheduling and onboard data logging. Two operating modes are implemented: (i) PI
Joshi, Gaurav, Adelman, Ian, Liu, Jun, Donnaway, Ruthie
AI model creation and System Verification of “Telemotion” Infrastructure-Cooperative Autonomous Driving System using Digital Twin2026-01-0255To be published on 04/07/2026
The concept of the vehicle has changed as a result of many innovations over the last decade in the fields of connected, autonomous/automated, shared, and electric (CASE) technologies. At the same time, labor shortages in Japan are becoming more serious due to a decline in the working population. To help resolve these issues, a remote-controlled autonomous vehicle driving system called Telemotion has been developed that automates the movement of vehicles in production plants. Telemotion is an autonomous driving and transportation system in which the recognition, judgment, and operation functions of driving are handled by a control system outside the vehicle that communicates wirelessly with the vehicle. This system utilizes artificial intelligence (AI) and other advanced technologies to realize safe unmanned autonomous driving, and is already in operation in production plants. Currently, efforts are under way to build a digital twin environment and conduct AI learning using computer
Hatano, Yasuyoshi, Iwazaki, Noritsugu, Nagafuchi, Yuhei, Iwahori, Kento, Tanaka, Atsushi, Uezu, Satoru, Kanou, Takeshi, INOUE, Go, Okamoto, Yuki, Oka, Yuhei, Kakuma, Daisuke, Chiba, Hiroya, Egashira, Kazuki, Ishikuro, Megumi, Sawano, Takuro
Analysis and Determination the Time to Rollover of Tractor Semi-Trailer Vehicle Based on Multi-body System Analysis and Newton–Euler Equations02-19-03-00133/27/2026
This article aims to determine the time to rollover (TTR) of a tractor semi-trailer vehicle (TSTV). It uses a full dynamics model for assessment, specifically applying multi-body system analysis and Newton–Euler Equations with a nonlinear tire model. The model is applied to investigate velocities ranging from 40 km/h to 80 km/h and magnitude of steering angles ranging from 12.5° to 300°. The times at which the Load Transfer Ratio (LTR), Roll Safety Factor (RSF), and lateral acceleration reach their maximum values are evaluated. The survey results demonstrate the impact of velocity and steering wheel angle on the time it takes for the LTR, RSF, and lateral acceleration to reach their maximum values. The time interval between the RSF reaching 1 and the LTR reaching 1 range from 0.144 s to 0.655 s. Similarly, the time it takes for the tractor body’s lateral acceleration to peak and the LTR to reach 1 varies between 0.228 s and 1.555 s. Additionally, the time interval from when the semi
Hung, Ta Tuan, Khanh, Duong Ngoc
Accuracy Requirements for the Road Friction Coefficient Estimation of a Friction-Adaptive Automatic Emergency Braking System as a Software Solution for Integrated Chassis Control Systems10-10-02-00163/10/2026
As part of this work, the accuracy requirements for the road friction coefficient estimation of a friction-adaptive automatic emergency braking (AEB) system are determined using a complex, nonlinear vehicle model. The AEB system varies its trigger distance depending on an estimated value of the road friction coefficient. The accuracy requirements are determined at a driving speed of 40 km/h depending on the severity classification of ISO 26262 in the statistically relevant Euro NCAP test scenario with a stationary target vehicle. MATLAB/Simulink is used as simulation software. The permissible estimation error (difference between estimated value and road friction coefficient) is determined by the severity classification S1 (light and moderate injuries). The results show that the positive permissible estimation error (road friction coefficient is overestimated) must not exceed about 30% of the road friction coefficient to comply with the severity classification S1 of ISO 26262.
Ahrenhold, Tim, Wielitzka, Mark, Binnewies, Tomas, Henze, Roman
Research on the Working Characteristics of Thermal Management System of Refrigerant Direct Cooling for Battery2026-01-70242/27/2026
This paper focus on the direct cooling plate with serpentine flow channels, the effects of heat load power, compressor speed, fan speed, and types of heating plates on the temperature field of the cold plate were investigated respectively based on the direct cooling thermal management system.The experimental results show that as the heating power decreases, both the overall temperature and temperature difference of the cold plate decrease synchronously. The temperature distribution along the flow channel is non-monotonic, with the highest temperature at the first elbow (T2/T3) and the lowest temperature at the outlet (T12), which is lower than the inlet temperature.A study on the T4-T11 region reveals that when the fan speed is low, with the increase of compressor speed, both Tmax and Tmin first decrease and then increase, while ΔT decreases. When the fan speed is constant at medium or high levels, as the compressor speed increases from low to medium, Tmax and Tmin decrease and ΔT also
Chen, Sijian, Huo, Guojun, Chen, Jiyong, Wei, Shaoliang, Zhang, Guihao, Zhang, Jinglong, Ju, Xinze, Yang, Xiaoxia
Development of Smart Climate Control with Multiple Level Auto Calibrations2026-28-00082/12/2026
Electric Vehicles (EVs) are rapidly transforming the automotive landscape, offering a cleaner and more sustainable alternative to internal combustion engine vehicles. As EV adoption grows, optimizing energy consumption becomes critical to enhancing vehicle efficiency and extending driving range. One of the most significant auxiliary loads in EVs is the climate control system, commonly referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a substantial portion of the battery's energy—especially under extreme weather conditions—leading to a noticeable reduction in vehicle range. This energy demand poses a challenge for EV manufacturers and users alike, as range anxiety remains a key barrier to widespread EV acceptance. Consequently, developing intelligent climate control strategies is essential to minimize HVAC power consumption without compromising passenger comfort. These strategies may include predictive thermal management, cabin pre-conditioning
Mulamalla, Sarveshwar Reddy, SV, Master Eniyan, M, Nisshok, Anugu, Anil, E A, Muhammed, Guturu, Sravankumar
An Integrated Model Approach for Predicting Vehicle Dynamics Performance in HiL Validation2026-28-00442/12/2026
In the automotive industry, increasing noise regulations are influencing product sales and passenger comfort, creating a need for more effective noise testing methods. Hardware-in-Loop (HiL) based virtual acoustic testing serves as a critical step before Driver-in-Loop testing, allowing for the assessment of vehicle performance and noise levels inside and outside the vehicle under various conditions before physical prototype testing is performed. The Hardware-in-the-Loop (HiL) simulator setup is equipped with joystick control that requires a physical representation of the vehicle dynamics model provided as a Functional Mock-up Unit (FMU) in real-time format. In contrast, the vehicle control logic is implemented in C++ code. The simulator incorporates both lateral and longitudinal dynamics. Additional interfaces are integrated to support joystick input and virtual road visualization enabling realistic vehicle maneuvering and dynamic performance evaluation. However, performing all test
Visuvamithiran, Rishikesan, Chougule, Sourabh, Srinivasan, Rangarajan, Laurent, Nicolas
AI Smart Timer-Based Power Control and Energy Monitoring System for Home Appliances2026-28-00602/12/2026
The paper presents the design and implementation of an AI-enabled smart timer-based power control and energy monitoring solution for household appliances. The proposed system integrates real-time sensing of electrical device parameters with cloud artificial intelligence for predictive analytics and automatic control. Continuous measurement of voltage, current and power consumption of the connected appliances are performed for analysis of the usage patterns. The appliance operation is completely automated by choosing between the best option which is the user-defined schedule or the load shifted schedule recommended by AI. The AI recommendation depends on peak demand of the day and the current load requirement thereby aiding approximate smoothening of daily load curve and improving load factor. The data collected is transmitted to the cloud for real-time and historical data collection, for prediction of consumption patterns, anomaly detection, and clustering appliances according to their
D, Anitha, D, Suchitra, Jain, Utsav, Maity, Souvik, Dinda, Atish
Miner’s Safety Bot: An ESP32-Based Autonomous Mobile Mine Safety Monitoring System2026-28-01162/12/2026
Mining operations are important to industrial growth, but they expose the mining workers to risk including hazardous gases, elevated ambient temperatures, and dynamic structural instabilities within underground environments. Safety systems in the past, typically based on fixed sensor networks or manual patrols, fall short in accurate hazard detection amidst shifting mine conditions. The proposed project Miner's Safety Bot advanced this paradigm by leveraging an ESP 32 microcontroller as a mobile platform that integrates gas sensing, thermal monitoring, visual inspection and autonomous obstacle avoidance. The system incorporates MQ7 semiconductor gas sensor to monitor real time carbon monoxide (CO), offering detection range from 5 to 2000 ppm with accuracy of 5 ppm. Temperature and humidity are monitored through DHT11 digital sensor, calibrated to ensure reliability across the harsh microclimates in mines. Navigation and autonomous movement are enabled by Ultrasonic Sensor (HC-SR04
D, Suchitra, D, Anitha, Muthukumaran, Balasubramaniam, Mohanraj, Siddharth, Subash Chandra Bose, Rohan
IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility2026-28-01132/12/2026
This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless
Deepan Kumar, Sadhasivam, S, Balakrishnan, Dhayaneethi, Sivaji, Boobalan, Saravanan, Abdul Rahim, Mohamed Arshad, S, Manikandan, R, Jamuna, L, Rishi Kannan
Research on Active Predictive Speed Control of Dual-Fuel Engine for Hardware Real-Time Applications03-19-01-00042/9/2026
The present article proposes an active observation speed prediction control algorithm architecture for embedded applications, with the aim of addressing the problems of complex operating conditions, strong perturbations, and high control real-time requirements of high-pressure direct injection (HPDI) dual-fuel engines. A nonlinear speed prediction model with diesel and natural gas injection mass as inputs has been established, and the nonlinear model predictive control (NMPC) method is used to realize the optimized control of engine speed. In order to enhance the operational efficiency of the algorithm on the embedded platform, a system has been developed that includes an event triggering mechanism and a warm-start strategy. These mechanisms work in tandem to dynamically adjust the computation cycle. Additionally, a torque reduced-order expansion state observer (RESO) has been integrated to improve the accuracy of perturbation estimation and computational efficiency. The model-level
Yang, Xinda, Li, Yunhua, Chen, Dongdong, Li, Yao, Zhang, Shutao, Zhao, Feiyang, Yu, Wenbin
Mitigating Urea Deposits in Selective Catalytic Reduction Systems through Software Control for Improved Performance in Low Duty Cycle Operations2026-26-04241/16/2026
After the implementation of BS-VI emission standards, effective exhaust after-treatment has become critical in minimizing harmful emissions from diesel engines. One significant challenge is the accumulation of hydrocarbons (HC) in the Diesel Oxidation Catalyst (DOC). Certain hydrocarbons may adsorb onto the catalyst surface yet remain unreactive, leading to potential operational inefficiencies. This phenomenon necessitates the desorption of unreactive hydrocarbons to allow space for more reactive species, thereby enhancing oxidation efficiency and overall catalyst performance. The process of desorption (DeSorb) is vital to maintaining the balance of reactive hydrocarbons within the DOC. When a vehicle is idling, unburnt fuel produces hydrocarbons that accumulate in the DOC. Upon acceleration, these hydrocarbons can lead to an uncontrolled rise in temperature, resulting in DOC push-out, catalyst damage, and downstream impacts on the Diesel Particulate Filter (DPF). To mitigate these
K, Sabareeswaran, K K, Uthira Ramya Bala, Raju, Manikandan, K J, Ramkumar, YS, Ananthkumar
HIL Simulation of Power Electronics Switching Circuit for Automotive Software Application2026-26-05161/16/2026
Power electronics switching applications are essential for energy management and conversion in automotive electric vehicles (EVs). This paper focuses on DC-DC converters, particularly the integration of 48V DC-DC converters in modern automotive systems. These converters are crucial for efficient power delivery to auxiliary systems such as infotainment, lighting, safety electronics, and thermal management units. In mild hybrid electric vehicles (MHEVs), 48V systems support advanced features like regenerative braking, electric turbocharging, and start-stop functionality. To ensure the reliability, safety, and performance of these converters, Hardware-in-the-Loop (HIL) testing has emerged as a powerful validation technique. HIL enables real-time simulation of the converter’s electrical environment and load conditions, allowing comprehensive testing of the control system without high-level voltage, significantly reducing development time, cost, and risk. The methodology involves utilizing
Yadav, Vikaskumar, Wakure, Vinod
Multi-Physics Simulation and Reliability Analysis of Onboard EV Charger2026-26-04991/16/2026
The rapid advancement of electric vehicle (EV) technology has created a demand for reliable and Thermal - efficient electronic components for power electronics and control systems on printed circuit boards (PCBs). The research looks at the overall simulation and study of a PCB for Electric Vehicles, including how it handles heat, stress, and reliability in real working conditions like considering casing (Heat Sink) in which PCB is held, into the simulation. We have used numerical based methods (reliability), Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) methods to simulate heat performance looking at steady-state and changing load profiles common in EV powertrains. We ran structural and thermal simulations to check the PCB's toughness against heat expansion and shaking loads often seen in cars. We also did a reliability check looking at heat cycling life for PCB components, and possible ways it could break to guess long-term toughness. The results show critical
Kanbarkar, Suraj Omana, Deore, Uday, Patil, Nishikant, Nayak, Shibabrata
Robust Validation of Rotary Switch with TFT Display in Hardware in Loop Simulation2026-26-05401/16/2026
Modern automotive systems are increasingly integrating advanced human-machine interfaces, including TFT displays, to enhance driver experience and functionality. Ensuring the reliability of these systems under diverse operating conditions is critical, especially given their role in vehicle control. This paper presents a Hardware-in-the-Loop (HIL) testing methodology for validation of rotary switch with TFT display. The HIL setup simulates real-world vehicle conditions, including CAN communication, power fluctuations and user interactions, enabling early detection of potential failure modes such as display flickering or communication loss. The results demonstrate improved robustness and reliability of the gear selection switch, supporting its deployment across multiple vehicle platforms.
Bhuyan, Anurag, Jahagirdar, Shweta, Khandekar, Dhiraj
Performance Enhancement and Safety Discharge Control Strategies for SPMSM in Automotive Applications2026-26-01791/16/2026
Surface Permanent Magnet Synchronous Motors (SPMSMs) have gained significant attention in modern industrial, automotive, and aerospace applications due to their high efficiency, power density, and superior dynamic performance. This paper explores the fundamental principles, control strategies, and optimization techniques for SPMSMs. The study focuses on advanced vector control methods, i.e., Field-Oriented Control (FOC), to achieve precise torque and speed regulation. Additionally, to ensure the safety and reliability of EV motors. Active discharge strategies used in EV motor drives focus on circuit topologies, control techniques, and implementation challenges. The paper also discusses a comparison of Sinusoidal Pulse Width Modulation (SPWM) and Space Vector Pulse Width Modulation (SVPWM) techniques, where the maximum speed of the motor is achieved. The findings highlight the potential of SPMSMs in high-performance applications, emphasizing future research directions in energy
Munnur, Swathi, Gandhi, Nikita, Tendulkar, Swati, Masand, Deepika, Murty, V. Shirish, Peruka, Mahesh
Validation of 0D-1D Co-Simulation Strategies in Cold Conditions for ‘Full EV Digital Vehicle’2026-26-04481/16/2026
Electric Vehicles (EV) are embedded with increased software algorithms coupled with several physical systems. It demands the efficacy of components which are linked together to build a system. The digital models reviewed in this paper are at system-level and full vehicle-level, comprising many components and control design, analysis, and optimization. Systems pertaining to each functionality such as, A/C (Air Conditioning) loop, E-Powertrain (Electric Powertrain), HEVC (Hybrid Electric Vehicle Controller), Cooling system, Battery Management System (BMS), Vehicle control system etc. together make an ‘Integrated Digital Vehicle.’ Fidelity of Intersystem co-simulation [AMESIM + SIMULINK] is key to validating thermal and energy strategies. This paper elucidates the correlation of Digital Vehicle compared to Test for Thermal Strategy in different driving scenarios and Energy management. Validation of Digital vehicle with 52kWh, 40kWh High Voltage Battery for Intercity Travel of Customer
Sarapalli Ramachandran, Raghuveeran, Srinivasan, Rangarajan, Saravanan, Vivek, Dutta, Souham, Pichon, Martin, Leclerc, Cedric, Guemene, Alexis-Scott
Simplified Simulation of Electric Vehicle Battery Packs Using Deep Learning Based Reduced Order Models2026-26-04031/16/2026
The increasing adoption of electric vehicles (EVs), efficient and accurate battery modeling has become crucial for reliable performance evaluation and control system design. However, maintaining high accuracy in simulations generally requires complex computations, which can limit real-time applicability and scalability. High-fidelity battery models often require significant computational time, making them unsuitable for real-time simulations and large-scale system integration. This paper presents the application of Simulink Reduced Order Models (ROM) to simplify the simulation of EV batteries while maintaining acceptable levels of accuracy. The EV simulation environment has been developed in MATLAB/Simulink to analyze Battery Management System (BMS) control system design and assess EV system level performance. This simulation platform consists of BMS and other important EV controller models and high-fidelity plant models for battery and powertrain systems. While these high-fidelity
Vernekar, Kiran
Driving Thermal Intelligence – Pioneering Digital Twin-Driven Hardware-In-Loop for Smart Climate Control Modules2026-26-03801/16/2026
Thermal comfort is increasingly recognized as a vital component of the in-vehicle user experience, influencing both occupant satisfaction and perceived vehicle quality. At the core of this functionality is the Climate Control Module (CCM), a dedicated embedded Electronic Control Unit (ECU) within automotive HVAC system [6]. The CCM orchestrates temperature regulation, airflow distribution, and dynamic environmental adaptation based on sensor inputs and user preferences. This paper introduces a comprehensive Hardware-in-the-Loop (HIL) [3] testing framework to validate CCM performance under realistic and repeatable conditions. The framework eliminates the dependencies on physical input devices—such as the Climate Control Head (CCH) and Infotainment Head Unit (HU)—by implementing virtual interfaces using real-time controller, and Dynamic System modelling framework for plant models. These virtual components replicate the behaviour of physical systems, enabling closed loop testing with high
More, Shweta, Shinde, Vivek, Turankar, Darshana, Patel, Dafiya, Gosavi, Santosh, Ghanwat, Hemant
Development of an Optimized XCP Software Stack for Real-Time Validation of Vehicle Control Units Using Next-Generation Communication Protocols2026-26-01901/16/2026
With the rapid adoption of electric vehicles (EVs), ensuring the reliability, safety, and cost-effectiveness of power electronic subsystems such as onboard chargers, DC-DC converters, and vehicle control units (VCUs) has become a critical engineering focus. These components require thorough validation using precise calibration and communication protocols. This paper presents the development and implementation of an optimized software stack for the Universal Measurement and Calibration Protocol (XCP), aimed at real-time validation of VCUs using next-generation communication methods such as CAN, CAN-FD, and Ethernet. The stack facilitates read/write access to the ECU’s internal memory in runtime, enabling efficient diagnostics, calibration, and parameter tuning without hardware modifications. It is designed to be modular, platform-independent, and compatible with microcontrollers across different EV platforms. By utilizing the ASAM-compliant protocol architecture, the proposed system
Uthaman, Sreekumar
Optimization of Tactile Vibrations in Agricultural Tractor Using Design of Experiments2026-26-03441/16/2026
One of agricultural tractors most important aspects is operator comfort. In addition to working long hours, tractor operators may be at risk for health problems due to vibrations and mechanical shocks. The tactile vibrations of a tractor are a major consideration when choosing one for agricultural use. This project's mandate includes a study of tractor vibration control problems. It is essential to investigate the governing system in order to determine the cause of the problem. Evaluating the vibrations transmitted via the tractor and using the design of experiments (DOE) approach to lessen vibrations on particular tactile regions were the study's goals. There are several measures currently under investigation which can be used to reduce the vibrations caused by resonance in this paper, these include reducing the natural frequency so as to be able to avoid resonance with the second order engine frequency and the damping coefficient; this will ensure the amplitude of vibration at
Baviskar, Shreyas, dhobale, Vishwajeet, Bhangare, Amit, Kunde, Sagar, Wagh, Sachin
Advanced Driver Assistance for Sunroof Misuse: Alarm Trigger and Speed Limiting System2026-26-00411/16/2026
Sunroof-equipped vehicles are gaining rapid popularity in India, especially among young and urban users. However, unsafe practices like occupants protruding through the sunroof during driving have led to increasing injuries and fatalities, particularly in sudden braking or collisions. This behavior, prohibited under the Motor Vehicles Act, remains an overlooked safety risk in today’s vehicles. This paper presents an industry-first innovation: an Automated Safety Alarm and Speed Control System designed to detect and prevent sunroof misuse. Using integrated photoelectric and infrared beam sensors, the system detects human extension beyond the sunroof boundary while the vehicle is in motion. Upon detection, it triggers a tiered safety response: an immediate dashboard warning, an audible alert if vehicle speed exceeds 15 km/h and an active speed limiter that restricts vehicle speed to 20 km/h until safe conditions are restored. This marks a shift from passive warnings to active vehicle
Padmanapan, Gopi, Yadav, Sanjeev
Mitigating Air Flow Sensor Signal Variability to Optimize Engine Operation in Passenger Cars2026-26-05121/16/2026
In order to control the engine performance which is driven by the strict emission regulations and customer request for the improved fuel economy, precise air intake measurement and fuel control system are essential. In the modern engines, the mass air flow sensor (MAF) acts an important role which provides a precise estimation of air flow from the clean side ducting of air intake system to engine control unit module (ECU). The hot wire mass air flow sensor are mounted on the clean side of the air intake system in order to protect the sensing element from the contamination and to extend their lifespan as well as maintain its accuracy. It is essential to maintain a steady and a uniform airflow at the sensing element of the MAF sensor for reliable sensor reading at different engine speeds and varying engine load. However, the physical limitations of engine packaging inside the engine bay, limits the sensor placement. Incorrect sensor mounting can lead to errors in the airflow estimation
Sonone, Sagar Dinesh, Zope, Mahesh, Kale, Vishal, Padmawar, Harshad, Sridhar, S, Kolhe, Vivek M, Panwar, Anupam
Conceptual Framework for Real-Time Battery Health Estimation in Automotive Digital Twins Using Reinforcement Learning2026-26-06581/16/2026
The explosive growth of electric vehicles (EVs) calls forth the need for smart battery management systems that can perform health monitoring and predictive diagnostics in real-time. The conventional battery modelling methods mostly do not cover the complicated, dynamic behaviors coming from different usage patterns. The study outlines a structure that would use Reinforcement Learning (RL)-based AI agent as a part of the Battery Electrical Analogy (BEA) simulation platform. With the help of the AI agent, different health parameters such as State of Health (SOH), State of Charge (SOC), and the signs of early thermal runaway can be predicted in real-time. The suggested design takes advantage of the simulation-based approach to have the agent learn and utilizes a decentralized cloud architecture suitable for scaling and reducing the response time. The RL agent performs an essential role in the process by tagging along with the continuous learning and the adjustment of the battery
Pardeshi, Rutuja Rahul, Kondhare, Manish, Sasi Kiran, Talabhaktula
Innovative Preconditioning Solutions for Efficient High-Current Battery Charging2026-26-01671/16/2026
In high-performance charging systems, managing higher currents is crucial for efficient battery charging. Elevated battery temperature is the main challenge for limiting the duration and effectiveness of high-current charging. Our proposal of control system addresses these barriers by optimizing charging time by maintaining optimal temperature ranges for the battery. This is achieved through innovative preconditioning solutions that are incorporated with active Battery cooling configurations. Our system features a unique preconditioning approach with dedicated active cooling circuit for the battery which will provide cooling to battery even though cabin HVAC (Heat Ventilation & Air-conditioning unit) is switched off. The active liquid cooling system ensures effective temperature management without additional energy consumption, while the dedicated Battery active liquid cooling system provides enhanced cooling capabilities for more demanding scenarios and preconditioning. By integrating
Badgujar, Pankaj Ravindra, Bhosale, Subhash, Dave, Rajeev
Accelerating Sunroof System Development with Modular Hardware-in-the-Loop Testing2026-26-05191/16/2026
This paper presents a novel Hardware-in-the-Loop (HiL) testing framework for validating panoramic Sunroof systems independent of infotainment module availability. The increasing complexity of modern automotive features—such as rain-sensing auto-close, global closure, and voice-command operation—has rendered traditional vehicle-based validation methods inefficient, resource-intensive, and late in the development cycle. To overcome these challenges, a real-time HiL system was developed using the Real time simulation, integrated with Simulink-based models for simulation, control, and fault injection. Unlike prior approaches that depend on complete vehicle integration, this methodology enables early-stage testing of Sunroof ECU behavior across open, close, tilt, and shade operations, even under multi-source input conflicts and fault conditions. Key innovations include the emulation of real-world conditions such as simultaneous voice and manual commands, sensor faults, and environmental
Ghanwat, Hemant, Lad, Aniket Suryakant, Joshi, Vivek, More, Shweta
Comparative Analysis of Pothole Depth Estimation Using LIDAR and Stereo Imaging with Polarized Lenses: A DOE Approach2026-26-02691/16/2026
Potholes are a common road hazard that significantly compromise road safety. Water filled potholes can be particularly dangerous. These hidden hazards may cause vehicles to hydroplane [1], leading to a loss of control and potential collisions. At night or in low visibility conditions, such potholes can appear deceptively shallow, increasing the risk of severe suspension damage or tire blowouts. Additionally, deep water intrusion can affect critical components such as the exhaust system, air intake, or electrical wiring, potentially leading to engine stalling or short circuits. This research proposes a novel approach for identifying and determining the depth of potholes, especially those that are filled with water. By integrating YOLO, cutting edge computer vision methods like stereo imaging and Lidar. We hope to create a system that can precisely detect and evaluate potholes' severity, reducing the risks connected to these road hazards. A structured 2k factorial Design of Experiment
Ashok, Deeksha, Kumar, Pradeep, Singh, Amandeep
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