Browse Topic: Active safety systems

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An Evaluation of the Performance of the Automatic Emergency Braking and Forward Collision Warning System in a 2020 Jeep Grand Cherokee During Daytime and Nighttime Conditions2025-01-8698To be published on 04/01/2025
Testing was conducted to evaluate the performance of the 2020 Jeep Grand Cherokee’s Forward Collision Warning (FCW) and Automatic Emergency Braking (AEB) systems at speeds between 35 and 70 miles per hour (mph). Two 2020 Jeep Grand Cherokee vehicles were utilized under varying testing conditions in order to evaluate the systems. Approximately 54 daytime tests utilizing a foam Stationary Vehicle Target (SVT) were conducted at speeds between 35 and 70 mph. Additionally, five daytime tests utilizing a custom-built deformable foam stationary vehicle target (DSVT) designed to withstand 60+ mph impact speeds were conducted at 60 mph. Twelve nighttime tests utilizing the SVT were run at speeds between 35 and 60 mph. Furthermore, 11 nighttime tests utilizing a live vehicle with its brake lights illuminated were run at 60 mph. Testing measured the Time to Collision (TTC) values of the visual/audible component of the forward collision warning that was presented to the driver. In addition, the
Harrington, Shawn, Lieber, Victoria, Nagarajan, Sundar Raman
Technical Paper
New concepts to improve energy absorption in Front End Structures2025-01-8748To be published on 04/01/2025
Through the last 30 years, vehicles have seen its safety increased massively, but consequently, they have also significantly grown in weight and size to accommodate the latest safety systems, including new crumple zones and structures to allow for more energy absorption through the vehicle. This has caused that those roads that were built more than a century ago in European cities are in some cases no longer fit for today’s vehicles, causing space problems in big and crowded areas. The SALIENT project, a co-funded project by the European Commission, aims to make our roads safer and reduce serious injuries and fatalities. To accomplish this, SALIENT presents innovative structural and vehicle concepts that are safer, lighter, circular, and smarter. This project focuses on creating new technologies and is currently developing, validating and testing a light front-end structure (FES), to enhance vehicle safety. It has adapted advanced light materials, improved manufacturing and joining
Garcia, Victor, Mensa, Genís, Kunter, Karlheinz, Wagner, Wolfgang, Velasco, Jorge, Elmarakbi, Mohab, Nossol, Patryk, Saleem, Arham, Ventosinos, Vanessa, Moldes, Miguel
Technical Paper
Collision avoidance system at urban intersections using V2X communication2025-01-8030To be published on 04/01/2025
Driving safely through urban intersections is quite challenging for self-driving vehicles due to complicated road geometries and the highly dynamic maneuvers of oncoming traffic, which can cause a high risk of collisions. Traditional onboard sensors like cameras, radar, and lidar give limited visibility in this environment. To overcome these limitations, this paper explores implementing a collision avoidance system at urban intersections utilizing cellular vehicle-to-everything (V2X) communication. The system leverages V2X map data to identify warning and collision zones and uses vehicle-to-vehicle (V2V) communication to estimate the maneuver types and paths of approaching vehicles. The system assesses collision probabilities by calculating the intersection points of predicted paths for the ego vehicle and oncoming traffic. Depending on the level of collision probabilities, the system generates a collision warning signal to the driver or activates emergency braking when necessary to
Park, Seo-Wook, Suresh, Raynier, Ailuri, Anusha
Technical Paper
Recommendation of Adaptive Safe Speed Limit for different driver Profiles using Deep Learning2025-01-8106To be published on 04/01/2025
Personalization and driver profiling have emerged as popular topics within the Advanced Driver Assistance Systems domain. Several features, like seat position adjustment and route selection deliver a customized experience for everyone. The systems often profile the driver and their preferences using Machine Learning algorithms to anticipate the needs of the driver. Within this field, personalization of speed limits is not explored to the fullest. Currently, speed limits are the same for all drivers, irrespective of their driving style. Even when the speed limits are designed to address adverse weather conditions, they do not consider the driver’s capabilities. Thus, we propose a novel idea to recommend a safe speed limit based on the driving style and the surrounding environmental conditions. The architecture consists of Long Short-Term Memory based neural networks that capture the driver’s traits based on risks of the environment. The system identifies if a driver is uncomfortable in
Perumal, Rathapriya, Chouhan, Madhvendra, Rangarajan, Rishi
Technical Paper
Study on the Impact of Vehicle Pitch During Braking on Pedestrian Leg Injuries2025-01-8741To be published on 04/01/2025
With the increasing prevalence of Automatic Emergency Braking Systems (AEB) in vehicles, their performance in actual collision accidents has garnered increasing attention. In the context of Active Emergency Braking (AEB) systems, the pitch angle of a vehicle can significantly alter the nature of collisions with pedestrians. Typically, during such collisions, the pedestrian's legs are the first to come into contact with the vehicle's front structure, leading to a noticeable change in the point of impact. Thus, to investigate the differences in leg injuries to pedestrians under various pitch angles of vehicles when AEB is activated, this study employs the Total Human Model for Safety (THUMS) pedestrian finite element model, sensors were established at the leg location based on the Advanced Pedestrian Legform Impactor (aPLI), and a corresponding vehicle finite element model was used for simulation, analyzing the dynamic responses of the pedestrian finite element model at different pitch
Hong, Cheng, YE, BIN, Zhan, Zhenfei, Liu, Yu, Wan, Xinming, Hao, Haizhou
Technical Paper
Machine Learning Based Lane Detection and Lateral Offset Estimation Model for Vehicle Following Applications2025-01-8020To be published on 04/01/2025
Precisely understanding the driving environment and determining the vehicle's exact position are crucial for the safe operation of Autonomous Vehicles. In the vehicle following systems that offer higher energy efficiency by precisely following a lead vehicle, the relative position of the ego vehicle to lane center is a key measure to a safe automated speed and steering control. This article presents a novel Enhanced Lane Detection technique with centimeter-level accuracy in estimating the vehicle offset from the lane center using the front-facing camera. Leveraging state-of-the-art computer vision models, the Enhanced Lane Detection technique utilizes YOLO-V8 image segmentation, trained on a diverse real-world driving scenarios dataset, to detect the driving lane. To measure the vehicle lateral offset, our model introduces a novel calibration method using nine reference markers aligned with the vehicle perspective and converts the lane offset from image coordinates to real-world
Karuppiah Loganathan, Nirmal Raja, Poovalappil, Aman, Naber, Jeffrey, Robinette, Darrell, Bahramgiri, Mojtaba
Technical Paper
Research on Control Algorithm for Active Rear Wheel Steering System of Passenger Cars2025-01-8755To be published on 04/01/2025
With the continuous advancement of automotive technology, the active rear wheel steering system, serving as a crucial component for enhancing vehicle handling performance and safety, has garnered extensive attention. In this paper, the control algorithm and performance optimization of the active rear wheel steering system are deeply studied. Firstly, a complete vehicle dynamics model is established, providing a theoretical foundation for the research on control algorithms. Subsequently, the optimal control theory is adopted to achieve the control of the rear wheel steering angle, and the LQR control strategy with variable weight coefficients is proposed in response to the linear and nonlinear variations of the vehicle tire sideslip characteristics. Finally, taking the 2WS vehicle, the proportional steering control strategy for the front and rear wheels, and the proportional feedforward + yaw rate feedback control strategy as references, modeling and joint simulation based on the CarSim
Zhang, Yi, Zheng, Hongyu, Kaku, Chuyo, Zong, Changfu
Technical Paper
Research on the Boundary of Pedestrian Leg Impactor Test in Low-Speed Passive and Active Conditions2025-01-8735To be published on 04/01/2025
With the widespread application of the Automatic Emergency Braking System (AEB) in vehicles, its impact on pedestrian safety has received increasing attention. However, after the intervention of AEB, the kinematic characteristics of pedestrian leg collisions and their corresponding biological injury responses also change. At the same time, in order to accurately evaluate the pedestrian protection performance of vehicles, the current assessment regulations generally use advanced pedestrian protection leg impactors (aPLI) and rigid leg impactors (TRL) to simulate the movement and injury conditions of pedestrian legs. Based on this, in order to explore the collision boundary conditions and changes in injury between vehicles and APLI and TRL leg impactors under the action of AEB, this paper first analyzes the current passive and active assessment conditions. Secondly, the simulation software LS-DYNA is used to build a finite element model of APLI and TRL impactor-vehicle collisions to
YE, BIN, Hong, Cheng, Wan, Xinming, Liu, Yu, Cheng, James, LONG, YONGCHENG, Hao, Haizhou
Technical Paper
A Proactive System to Anticipate and Store the Damage Information on a Parked Car using Machine Learning Algorithm2025-01-8211To be published on 04/01/2025
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 also 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
Debnath, Sarnab, PATIL, Prasad, Belur Subramanya, Sheshagiri, Govinda, Shiva Prasad
Technical Paper
Do aftermarket and original equipment forward collision warning systems respond the same to vehicles and pedestrians2025-01-8677To be published on 04/01/2025
Automatic emergency braking and forward collision warning (FCW) systems are equipped to about 32% of registered vehicles in the U.S (Highway Loss Data Institute, 2024). Retrofitting vehicles with aftermarket devices can accelerate the adoption of FCW, but it is unclear if aftermarket systems perform similarly to original equipment manufacturer (OEM) systems. The performance of four aftermarket windshield-mounted FCW systems was evaluated in various Insurance Institute for Highway Safety (IIHS) rear-end and pedestrian crash avoidance tests and compared with previously tested OEM systems. The presence and timing of FCWs were measured when vehicles approached a stationary passenger car at 20,40,50,60 and 70 km/h, motorcycle and dry van trailer at 50,60, and 70 km/h, adult pedestrian at 40 and 60 km/h, and child pedestrian crossing the road at 20 and 40 km/h. Equivalence testing was used to determine if FCW performance was similar between aftermarket and OEM systems. OEM systems provided a
Kidd, David, Floyd, Philip, Aylor, David
Technical Paper
Daytime and Nighttime Performance Evaluations of the Automatic Emergency Braking and Forward Collision Warning Systems on a 2020 and 2022 Kia Telluride2025-01-8699To be published on 04/01/2025
Testing was conducted in daytime and nighttime conditions to evaluate the performance of the Automatic Emergency Braking and Forward Collision Warning systems present on both a 2020 and 2022 Kia Telluride. The 2022 Kia Telluride was tested during the day at speeds between 35 and 70 miles per hour, while the 2020 Kia Telluride was tested both during the day and at night at speeds between 35 and 60 miles per hour (mph). The daytime testing of both the 2020 and 2022 Kia Telluride utilized a foam stationary vehicle target. The nighttime testing of the 2020 Kia Telluride utilized a live Chevrolet Tahoe as the target. Testing measured the Time to Collision (TTC) values of the visual/audible component of the forward collision warning that was presented to the driver. Further, testing also quantified the timing of the two-phase response of the Automatic Emergency Braking (AEB) system. The results of both sets of testing add higher speed Forward Collision Warning (FCW) and AEB testing scenarios
Harrington, Shawn, Patrick-Moline, Peyton
Technical Paper
Different methods-based Curvature estimation and its effect on the Lane centering performance2025-01-8055To be published on 04/01/2025
ADAS (Advanced Driver Assistance Systems) lateral feature performance heavily depends on inputs from the surroundings as well as vehicle state information/signals. One critical piece of vehicle state information is curvature, which significantly impacts performance. Vehicle curvature is often used as an input for lateral trajectory generation and is also a component of the lateral motion controller. However, obtaining vehicle curvature information is not straightforward, as there is rarely a sensor that directly measures vehicle curvature. Instead, various vehicle signals and information are used to calculate curvature, sometimes employing advanced estimation methods. In this paper, we investigate the impact on lateral feature performance when vehicle curvature is estimated from different source signals. We examine the issues and benefits associated with different techniques for obtaining ego vehicle curvature. Additionally, we present a method to address these issues and discuss how
Awathe, Arpit, Varunjikar, Tejas, Jain, Arihant
Technical Paper
Leveraging Traffic-in-the-Loop Simulations to Assess the Impact of Traffic on Vehicle Energy Consumption2025-01-8054To be published on 04/01/2025
The advancements in vehicle connectivity and the increased level of driving automation can be leveraged for the development of advanced driver assistance systems (ADAS) that improve driver safety and comfort while optimizing the energy consumption of the vehicle. In the development phase of energy-efficient ADAS, modeling and simulation are used to assess the potential benefits of these technologies on energy consumption. However, there is a lack of standardized simulation or test frameworks to quantify the benefits. Moreover, the driving scenario and the traffic conditions are often not explicitly modeled when simulating energy-efficient ADAS, even though they have a major impact on the attainable energy benefits. This paper presents the development and implementation of a closed-loop traffic-in-the-loop simulator designed to evaluate the performance of vehicles under realistic traffic conditions. The primary objective is to assess how varying traffic conditions influence vehicle
Grano, Elia, Villani, Manfredi, Ahmed, Qadeer, Carello, Massimiliana
Technical Paper
Impact of Connected and Automated Vehicles on Longitudinal and Lateral Performance of Heterogeneous Traffic Flow in Shared Autonomy on Two-Lane Highways2025-01-8098To be published on 04/01/2025
Intelligent transportation systems and connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. Consequently, traditional human-driven vehicles (HDVs), CAVs, and human-driven connected and automated vehicles (HD-CAVs) will coexist on the roads for the foreseeable future. Simultaneously, car-following behaviors in equilibrium and discretionary lane-changing behaviors make up the most common highway operations, which seriously affect traffic stability, efficiency and safety. Therefore, it’s necessary to analyze the impact of CAV technologies on both longitudinal and lateral performance of heterogeneous traffic flow. This paper extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using the quintic polynomial curve to account for different vehicle types, considering human factors and cooperative adaptive cruise control. Then, this paper incorporates CAV penetration rates, shared autonomy
Wang, Tianyi, Guo, Qiyuan, He, Chong, Li, Hao, Xu, Yiming, Wang, Yangyang, Jiao, Junfeng
Technical Paper
Analysis of OEM Collision Avoidance Data Outputs during VRU Testing2025-01-8053To be published on 04/01/2025
Using OEM tools to analyze data available from on-vehicle collision avoidance systems can shed light on the data scope that is available to analyze failures in these systems. In this work, a Toyota Safety Sense 3.0 vehicle was tested to determine the performance in several collision imminent scenarios. Vehicle data was pulled and compared to data collected from instrumentation to determine the vehicle system accuracy in detecting targets, investigate timings, and understand the scope of data available to an agency investigating a failure. Images and data files are presented as an example of scope of output. Tests included several variable overlap tests, along with several tests specifically chosen due to compare performance across the operating design domain
Bartholomew, Meredith, Heydinger, Gary
Technical Paper
Compensation Algorithm Development in Platform for Pedestrian ADAS Testing2025-01-8066To be published on 04/01/2025
Test procedures such as EuroNCAP, NHTSA’s FMVSS 127, and UNECE 152 all require specific pedestrian to vehicle overlaps. These overlap variations allow the vehicle differing amounts of time to respond to the pedestrian’s presence. In this work, a compensation algorithm was developed to be used with the STRIDE robot for Pedestrian Automatic Emergency Braking tests. The compensation algorithm uses information about the vehicle’s speed and position to determine whether the robot needs to move faster or slower in order to properly overlap the vehicle. Tests of the algorithm include two different methodologies for testing functionality and the use of a robotic test driver to vary the vehicle speed in a consistent manner
Bartholomew, Meredith, Nguyen, An, Helber, Nicholas, Heydinger, Gary
Technical Paper
Lane-Keeping for SAE Level 3 Autonomous Vehicles: A Behavior-Tree Approach Using Lidar Data for Enhanced Safety and Control2025-01-8025To be published on 04/01/2025
Lane-keeping is a critical function for SAE Level 3 autonomous vehicles, requiring rigorous validation and end-to-end interpretability. All recently U.S. approved level 3 vehicles are equipped with lidar, likely meant to accelerate active safety regulation compliance. Lidar offers precise and reliable surface distance and intensity measurements, enabling more effective implementation of rule-based algorithms compared to camera-based methods, which tend to rely on machine learning based methods for state-of-the-art performance. This paper presents a behavior-tree algorithm for lane-keeping using data from an Ouster OS0 lidar sensor. The algorithm evaluates factors such as road curvature, speed limits, road types (rural, urban, interstate), and the proximity of objects or humans to lane markings. It also accounts for the behavior and type of adjacent and opposing vehicles, lane occlusion, and weather conditions. The algorithm outputs safe lane keeping control instructions. The algorithm
Soloiu, Valentin, Mehrzed, Shaen, Kroeger, Luke, Pierce, Kody, Sutton, Timothy, Lange, Robin
Technical Paper
Numerical Investigation of Fuel Injection Strategies for Ammonia-Diesel Dual-fuel Engine2025-01-8368To be published on 04/01/2025
This study numerically investigates ammonia-diesel dual fuel combustion in a heavy-duty engine. Detailed and reduced reaction mechanisms are validated against experimental data to develop injection timing maps aimed at maximizing indicated thermal efficiency (ITE) while mitigating environmental impacts using stochastic reactor model (SRM). The equivalence ratio, ammonia energy share (AES), injection timing, and engine load are varied to optimize combustion efficiency and minimize emissions. The results demonstrate that advancing injection timing reduces ITE due to heightened in-cylinder temperatures, resulting in increased heat losses through walls and exhaust gases. Maximum chemical efficiency is observed at an equivalence ratio near 0.9 but decreases thereafter, influenced by ammonia’s narrow flammability range. Emission analysis highlights significant reductions in Global Warming Potential (GWP) and Eutrophication Potential (EP) with higher AES, driven by decreased CO2 and nitrogen
Karenawar, Shivraj Anand, YADAV, Neeraj Kumar, Maurya, Rakesh Kumar
Technical Paper
Responsible AI System Development in Automotive Applications: A Framework2025-01-8102To be published on 04/01/2025
With the surge in adoption of artificial intelligence (AI) in automotive systems, especially Advanced Driver Assistance Systems (ADAS) and autonomous vehicles (AV), comes a proportional surge of AI-related incidents–several of which have ended in injuries and fatalities. These incidents all share a common deficiency: insufficient coverage towards safety, ethical, and/or legal requirements. Responsible AI (RAI) is an approach to developing AI-enabled systems that systematically takes such requirements into account. Existing international standards like ISO 21448:2022 (Safety of the Intended Functionality) do offer guidance in this regard but are not sufficient. Therefore, several industry-agnostic and domain-specific technical standards are emerging concurrently to address various RAI-related challenges, including but not limited to ISO/CD PAS 8800 for the integration of AI in automotive systems, ISO/IEC 5469:2024 for the integration of AI in functional safety, ISO/IEC 42001:2024 for
Nelson, Jody, Lin, Christopher
Technical Paper
Integration of Safety Standards for a Unified Safety Framework for Automated Vehicles2025-01-8717To be published on 04/01/2025
This paper proposes a structured safety framework tailored for the concept phase of Level 2 and Level 3 automated vehicles, addressing the unique challenges posed by these advanced systems. The framework integrates key principles from ISO 26262 and ISO 21448 to create a safety approach that spans hardware reliability, functional safety, and system performance. Central to the framework is a broad analysis that combines methodologies from System-Theoretic Process Analysis (STPA) and Hazard Analysis and Risk Assessment (HARA). This dual approach enables the identification of potential risks arising from both hardware failures and the intended functionalities of the system. The framework further details a combined specification and design process that aligns the strengths of each standard, ensuring robust sensor architectures and reliable decision-making processes. A case study on Adaptive Cruise Control (ACC) with Lane Keeping (LK) is presented to demonstrate the practical implementation
Sari, Ayse Aysu, Soleimani, Morteza
Technical Paper
High-Speed FCW & AEB Testing of Tesla Model 3's Across Software Updates2025-01-8695To be published on 04/01/2025
A total of 148 tests were conducted to evaluate the Forward Collision Warning (FCW) and Automatic Emergency Braking (AEB) systems in five different Tesla Model 3 vehicles between model years 2018 and 2020 across four years. These tests involved stationary targets, including a foam Stationary Vehicle Target (SVT), a Deformable Stationary Vehicle Target (DSVT), a live vehicle with brake lights, and a SoftCar360 designed for high-speed impact tests. The evaluations were conducted at speeds of 35, 50, 60, 65, 70, 75, and 80 miles per hour (mph) during both daytime and nighttime conditions and utilized early and medium FCW settings. These settings, integral to Tesla's Collision Avoidance Assist, modify object detection and timing of visual and auditory warnings issued to drivers. The 2018 to 2020 vehicles initially utilized both cameras and radar for object detection. Tesla updated their software and detection algorithm, and thus, vision-only tests were able to be conducted as a comparison
Harrington, Shawn, Nagarajan, Sundar Raman
Technical Paper
An Overview and Testing of Pedal Misapplication Systems2025-01-8705To be published on 04/01/2025
Testing was conducted to evaluate the performance of Pedal Misapplication systems in five different vehicles in a simulated collision scenario. A 2023 BMW iX, 2023 Subaru Ascent, 2020 Tesla Model 3, 2020 Subaru Outback, and a 2014 Subaru Forester were tested in a simulated pedal misapplication scenario, whereby the test vehicle's accelerator was quickly depressed to 100% with a stationary pedestrian and vehicle target in front of the test vehicle. The testing involved measuring the performance of the respective vehicles' pedal misapplication systems, including when or if the throttle was cut, whether a forward collision warning was provided, or if the vehicle automatically braked to a stop. Results of the five vehicles tested will be presented and compared
Harrington, Shawn, Takbhate, Shubham Ramraja, Martin, Nicholas, Handzic, Dino, Patrick-Moline, Peyton
Technical Paper
Active and passive security generalization algorithm and typical case analysis2025-01-8744To be published on 04/01/2025
In order to be able to effectively predict the safety performance of vehicles and reduce the cost of enterprise safety tests, a generalized model for automotive active and passive safety simulation is proposed. The frontal driver's side collision model under AEB intervention is created by MADYMO software, and the collision acceleration obtained from the bench test is used as the model input to simulate the human body in different seating postures, and the damage values of each part of the Hybrid III 50th dummy are read, and the active-passive simulation model is established by BP neural network according to the correlation relationship between the two, and the model input is the inclination angle centered at the waist of the dummy, and the output is the damage values of each part of the dummy. The input of the model is the tilt angle centered on the waist of the dummy, and the output is the damage value of each part of the dummy. The results surface: the prediction accuracy is more
Ge, Wang
Technical Paper
A study on affections of active neck muscle force on neck injury responses in frontal impact with Automatic Emergency Braking conditions2025-01-8742To be published on 04/01/2025
Neck injury is one of the most common injuries in traffic accidents, and its severity is closely related to the posture of the occupant at the time of impact. In the current era of smart vehicle, the triggered AEB and the occupant's active muscle force will cause the head and neck to be out of position which has significant affections on the occurrence and severity of neck injury responses. Therefore, it is very important to study the influences of active muscle force on neck injury responses in in frontal impact with Automatic Emergency Braking conditions. Based on the geometric characteristics of human neck muscles in the Zygote Body database, the reasonable neck muscle physical parameters were obtained firstly. Then a neck finite element model (FEM) with active muscles was developed and verified its biofidelity under various impact conditions, such as frontal, side and rear-end impacts. Finally, using the neck FEM with or without active muscle force, a comparative study was
Junpeng, Xu, Gan, Qiuyu, Jiang, Binhui, Zhu, Feng
Technical Paper
Integrated Bracket Design Optimizations to Resolve Issues in Vehicle Prototype Stage2025-01-8614To be published on 04/01/2025
Integrated bracket is a plastic part which packages functional components such as ADAS, Rain Light Sensor and the mounting provisions of auto dimming IRVM. This part is fixed on the windshield of an automobile using double sided 3M tapes and glue. ADAS, rain light sensor and auto dimming IRVM play an important part in the safety of driver, and everyone present in the automobile. The paper takes the readers through a concern faced by the authors that emphasizes the importance of the combination and location of tapes and glues on the integrated bracket. The 3M tapes at the B side of the integrated bracket are used to locate it on the windshield and hold it until the glue's curing time gets completed. If the combination and positions of glue and tapes is not optimum, then the bracket will get peeled off from the windshield causing aesthetic deterioration. This concern came in the prototype stage of a Mahindra SUV. In this concern, the integrated bracket was peeling off from the windshield
Chandravanshi, Priyansh, Dharmatti, Girish
Technical Paper
Tire-Road Friction Estimation and Classification Based on a CNN using Tire Acoustical Signals for Autonomous Driving Vehicles2025-01-8761To be published on 04/01/2025
With the development of advanced driver assistance system (ADAS) and autonomous driving technologies, the need for research on vehicle state recognition has increased. However, research on Estimation and Prediction of the Tire-Road Friction and Classification, which is one of the most important factors for vehicle state recognition, has not yet been conducted. If Estimation and Prediction of the Tire-Road Friction and Classification are possible, the control system can make a more robust decision by validating the information from other sensors. Therefore, estimation and Prediction of the Tire-Road Friction and Classification is essential. To achieve this, tire–pavement interaction noise (TPIN) is adopted as a data source for Estimation and Prediction of the Tire-Road Friction and Classification. Accelerometers and vision sensors have been used in conventional approaches. The disadvantage of acceleration signals is that they can only represent the surface profile properties and are
Yoon, Youngsam, Kim, Hyungjoo, Lee, Sang Kwon, Lee, Jaekil, Hwang, Sunguk, Ku, Sehwan
Technical Paper
Advancing Verification and Validation for ADAS and ADS2025-01-8052To be published on 04/01/2025
Emerging automotive technologies like advanced driver assistance systems (ADAS) and automated driving systems (ADS) hold promise for improving safety for the traveling public; however, effective verification and validation (V&V) of these systems has proven to be challenging. Traditional testing methodologies may serve in limited cases for systems exhibiting low levels of automation, but recent studies show that these systems that have been brought to market perform poorly in practice. Further, these traditional methods do not serve for testing systems with high levels of automation where a human driver simply serves as a fallback ready user or is out of the loop altogether. New V&V methods are required to assess whether these systems can perform their intended functions in their intended operating environments, and to assess whether they can do so safely across the expansive and variable operating space. This paper presents an overview of ADAS and ADS challenges and novel approaches to
Thorn, Eric, Knisley, Veronica, Auchter, Joseph
Technical Paper
Effect of Aftermarket Modifications on ADAS Functionality - 2021 Ford F-150 Light Vehicle2025-01-8057To be published on 04/01/2025
Both the automotive aftermarket and Advanced Driver Assistance Systems (ADAS) are growing. However, there is very little information available in the public domain about the effect of aftermarket modifications on ADAS functionality. To address this deficiency, a research study was previously performed in which a 2022 Chevrolet Silverado 1500 light truck was tested in four different hardware configurations. These included stock as well as three typical aftermarket configurations comprised of increased tire diameters, a level kit, and two different lift kits. Physical tests were carried out to investigate ADAS performance of lane keeping, crash imminent braking, traffic jam assist, blind spot detection, and rear cross traffic alert systems. The results of the Silverado study showed that the ADAS functionality of that vehicle was not significantly altered by aftermarket modifications. To determine if the results of the Silverado study were significant only for that particular light truck
Bastiaan, Jennifer, Muller, Mike, Morales, Luis
Technical Paper
Advanced Driver Assistance Systems (ADAS): Assessing the Efficacy of Non-Impact Testing for Evaluating the Performance of Frontal Collision Mitigation Technology2025-01-8056To be published on 04/01/2025
As Automatic Emergency Braking (AEB) systems become standard equipment in light duty vehicles, the ability to evaluate these systems efficiently is becoming critical to regulatory agencies and manufacturers. One of the key drivers of the cost and time required to evaluate the performance of these systems is the potential collision between the subject vehicle and the test target. AEB performance can vary significantly between different scenarios, especially with high closing speeds. Consequently, high speed impacts that may occur while evaluating the performance of an AEB system can result in significant damage to both the subject vehicle and the test equipment, and result in significantly higher test costs. For tests in which impact with the test target is not acceptable, an alternative methodology is often used: steering is applied by the driver to avoid the target prior to a potential collision. A test series was performed to determine if this alternative evaluation methodology can
Alexander, Ross, Kuykendal, Michelle, Easter, Casey, Koszegi, Giacomo, Paradiso, Marc, Scally, Sean
Technical Paper
Real-time Dynamic mass learning algorithm for connected and automated light-duty vehicles2025-01-8389To be published on 04/01/2025
Mass estimation in light-duty vehicles (LDVs) is a crucial aspect of vehicle dynamics, control systems, energy optimization, range prediction, and overall performance. Accurate mass estimation is essential for precise energy predictions, which are used by energy optimization algorithms. It also enhances vehicle safety and the effectiveness of advanced driver assistance systems (ADAS). The mass of a vehicle can vary depending on occupancy and load. This paper presents a comprehensive study on in-situ mass learning in light-duty vehicles under real-world driving conditions. Utilizing simple longitudinal dynamics, road grade calculated from GPS with RTK correction, and the vehicle’s torque model, we developed a robust framework for vehicle mass estimation. A detailed sensitivity analysis was performed to evaluate the impact of uncertainties or errors in various inputs and parameters, identifying optimal regions for learning the mass and ensuring the model's reliability. This method was
Poovalappil, Aman, Robare, Andrew, Apostol, Peter, Bahramgiri, Mojtaba, Chen, Bo, Naber, Jeffrey, Robinette, Darrell
Technical Paper
Evaluating the Impact of Sensor Accuracy on Adaptive Cruise Control Performance: A Simulation Study2025-01-8063To be published on 04/01/2025
Adaptive Cruise Control (ACC) is an advanced driver assistance system that manages a vehicle's longitudinal motion. Its performance heavily depends on the accuracy of the sensors used. While ACC algorithms are designed to optimize performance, the effectiveness of the ACC system is significantly influenced by sensor accuracy and performance. Although the key performance indicator (KPI) of the ACC system is crucial for evaluating its effectiveness, quantifying the exact impact of sensor accuracy on this KPI presents challenges. This paper investigates the effect of sensor accuracy on ACC performance. We conducted a simulation study to replicate the sensor accuracy observed in actual vehicles and analyze its impact on ACC performance. Additionally, the paper explores potential solutions to mitigate performance variations caused by sensor accuracy variability
Awathe, Arpit, Varunjikar, Tejas, Raut, Abhinandan Vijay, Patel, Darsh
Technical Paper
Modular Dynamometer Testing Framework to Evaluate Energy Impacts of Longitudinal Automated Driving Systems2025-01-8065To be published on 04/01/2025
As longitudinal Automated Driving System (ADS) technologies, such as Adaptive Cruise Control (ACC), become more prevalent, robust testing frameworks that encompass both simulation and vehicle-in-the-loop (VIL) methodologies are essential to ensure system reliability, safety, and performance refinement. Although significant research has focused on ACC algorithm development and simulation testing, existing VIL dynamometer testing frameworks are typically tailored to specific vehicle models and sensor simulation tools. These highly customized approaches often fail to account for broader interoperability while overlooking energy consumption as a key performance metric. This paper presents a novel modular framework for ACC dynamometer testing, designed to enhance interoperability across a diverse range of vehicle platforms, simulation tools, and dynamometer facilities with a focus on evaluating impact of automated longitudinal control on the overall energy consumption of the vehicle. The
Goberville, Nicholas, Hamilton, Kayla, Di Russo, Miriam, JEONG, Jongryeol, Das, Debashis, Ord, David, Misra, Priyashraba, Crain, Trevor
Technical Paper
Development of a Computer-Less Robotic Platform for ADAS Testing2025-01-8064To be published on 04/01/2025
Testing collision avoidance systems on vehicles has become increasingly complex. Robotic platforms require GPS, network communications, tuning, and ever-increasing scope to the user interface to function. As an alternative to these complicated systems, but as an improvement to a pulley system, a simplistic robotic platform was developed. A computer-less user interface was designed and developed, and a series of tests were performed to evaluate the effectiveness of the robot in performing basic, repeatable straight-line tests with a vehicle in the loop. The vehicle’s on-board data outputs were additionally considered as a means to provide test outputs should data be needed. Based on the testing outcomes, the development of further control algorithms, user requirements, and the prototype improvements are analyzed for future work
Bartholomew, Meredith, Muthaiah, Ponaravind, Heydinger, Gary
Technical Paper
Role of AI in an Autonomous Vehicle Perception Systems: Use of Convolutional Neural Network Approach to Design the Vehicle Perception System2025-01-8012To be published on 04/01/2025
Artificial Intelligence has gained lot of traction and importance in the 21st century with use cases ranging from Speech recognition, Learning, Planning, Problem solving to search engines etc. Artificial Intelligence also has played a key role in the development of Autonomous Vehicles and robots ranging from perception, localization, decision to controls. Within the big AI umbrella there is Machine learning which is all about using your computer to "learn" how to deal with problems without “programming". Deep learning is a branch of machine learning based on a set of algorithms that learn to represent the data directly from the input such as an Image, text, Sound, etc. Within deep learning there are Convolutional Neural Networks and Recurrent Neural Networks (CNN/RNN). The study here used convolutional neural network approach to perform image/object recognition. Given that the objective of the autonomous or semi-autonomous vehicle is to promote safety and reduce number of accidents, it
Mansour, Iyad, Singh, Sanjay
Technical Paper
Multi-Band Bayesian Particle Filter for Occupancy Grid Mapping2025-01-8011To be published on 04/01/2025
In the realm of advanced driver assistance systems and autonomous vehicles, the ability to accurately perceive and interpret the vehicle's environment is not just a requirement - it's the cornerstone upon which all functionality is built. One prevalent method of representing the environment is through the use of an occupancy grid map. This map segments the environment into distinct grid cells, each of which is evaluated to determine if it is occupied or free. This evaluation operates under the assumption that each grid cell is independent of the others. The underlying mathematical structure of this system is the binary Bayes filter (BBF). The BBF integrates sensor data from various sources and can incorporate measurements taken at different times. The occupancy grid map does not rely on the identification of individual objects, which allows it to depict obstacles of any shape. This flexibility is a key advantage of this approach. This study introduces a novel approach to dynamic grid
Wani, Ankit Shrikrishna
Technical Paper
Empirical Analysis on Machine Vision Recognition of Green Bike Lanes for Vulnerable Road Users Safety2025-01-8017To be published on 04/01/2025
Deliberate modifications to infrastructure can significantly enhance machine vision recognition of road sections designed for Vulnerable Road Users, such as green bike lanes. This study evaluates how green bike lanes, compared to unpainted lanes, enhance machine vision recognition and vulnerable road users safety by keeping vehicles at a safe distance and preventing encroachment into designated bike lanes. Conducted at the American Center for Mobility, this study utilizes a vehicle equipped with a front-facing camera to assess green bike lane recognition capabilities across various environmental conditions including dry daytime, dry nighttime, rain, fog, and snow. Data collection involved gathering a comprehensive dataset under diverse conditions and generating masks for lane markings to perform comparative analysis for training Advanced Driver Assistance Systems. Quality measurement and statistical analysis are used to evaluate the effectiveness of machine vision recognition using
Ponnuru, Venkata Naga Rithika, Das, Sushanta, Grant, Joseph, Naber, Jeffrey, Bahramgiri, Mojtaba
Technical Paper
Synchronizing Real-Time Control in Centralized Automotive E/E Architectures2025-01-8072To be published on 04/01/2025
Abstract: Automotive technologies have been rapidly evolving with the introduction of electric powertrains, Advanced Driver-Assistance System (ADAS) and Over-The-Air (OTA) upgradability. Existing decentralized architectures are not an optimal choice for these applications, due to significant increases in cost and complexity. The transition to centralized architectures enables heavy computation to be delegated to a limited number of powerful Electronic Control Units (ECUs) called domain or zone controllers. The remaining ECUs, known as smart actuators, will perform well defined and specific tasks, receiving new parameters from the dedicated domain/zone controller over a network. Net- work bandwidth and time synchronization are the two major challenges in this transition. New automotive standards have been developed to address these challenges. Automotive Ethernet and Time Sensitive Networking (TSN) are two standards that are well-suited for centralized architectures. This paper presents
Ayesh, Mostafa, Bandur, Victor, Pantelic, Vera, Wassyng, Alan, Wasacz, Bryon, Lawford, Mark
Technical Paper
RGB2BEV-Net: A PyTorch-based End-to-End Pipeline for RGB to BEV Segmentation Using an Extended Dataset for Autonomous Driving2025-01-8023To be published on 04/01/2025
In this work, we introduce RGB2BEV-Net, an end-to-end pipeline that extends traditional BEV segmentation models by utilizing raw RGB images with Bird’s Eye View (BEV) generation. While previous work primarily focused on pre-segmented images to generate corresponding BEV maps, our approach expands this by collecting RGB images alongside their affiliated segmentation masks and BEV representations. This enables direct input of RGB camera sensors into the pipeline, reflecting real-world autonomous driving scenarios where RGB cameras are commonly used as sensors, rather than relying on pre-segmented images. Our model processes four RGB images through a segmentation layer before converting them into a segmented BEV, implemented in the PyTorch framework after being adapted from an original implementation that utilized a different framework. This adaptation was necessary to improve compatibility, optimize performance and ensure better integration of the entire system within autonomous vehicle
Hossain, Sabir, Lin, Xianke
Technical Paper
Improved Distance Estimation in Dynamic Environments through Multi-sensor Fusion with Extended Kalman Filter2025-01-8034To be published on 04/01/2025
The application of multi-sensor fusion for enhanced distance estimation accuracy in dynamic environments is crucial for advanced driver assistance systems (ADAS) and autonomous vehicles. Limitations of single sensors such as camera or radar in adverse conditions motivate the use of combined camera and radar data to improve reliability, adaptability, and object recognition. A multi-sensor fusion approach using an Extended Kalman Filter (EKF) is proposed to combine sensor measurements with a dynamic system model, achieving robust and accurate distance estimation. The research utilizes the Mississippi State University Autonomous Vehicular Simulator (MAVS) to create a controlled environment for data collection. Data analysis is performed using MATLAB. Qualitative (visualization of fused data vs ground truth) and quantitative metrics (RMSE, MAE) are employed for performance assessment. Initial results with simulated data demonstrate accurate distance estimation compared to individual
Ebu, Iffat Ara, Islam, Fahmida, Rafi, Mohammad AbdusShahid, Iqbal, Umar, Rahman, Mahfuzur, Ball, John
Technical Paper
Integrated Driver Health Monitoring System With Real-time Alerts Using In-Cabin Sensors and Vehicle Telematics2025-28-0195To be published on 02/07/2025
Automotive industries focus on driver safety leading to raising improvements and advancements in Advanced Driver Assistance Systems (ADAS) to avoid collisions and provide safety and comfort to the drivers. This paper proposes a novel approach toward Driver health and fatigue monitoring systems that uses cabin cameras and biometric sensors communicating continuously with vehicle telematics systems to enhance real-time monitoring and predictive intervention. The data from the camera and biometric sensors is sent to the machine learning algorithm (LSboost) which processes the data and if anything is wrong concerning the driver's behavior then immediately it communicates with vehicle telematics and sends information to the emergency services. This approach enhances driver safety and reduces accidents caused due to health-related driver impairment. This system comprises several sensors and fusion algorithms are applied between different sensors like cabin camera and biometric sensors, all
Bhargav, Matavalam
Technical Paper
Software Architecture for Autonomous Vehicles Using Matlab Simulink2025-28-0190To be published on 02/07/2025
Autonomous vehicles utilise sensors, control systems and machine learning to independently navigate and operate through their surroundings, offering improved road safety, traffic management and enhanced mobility. This paper details the development, software architecture and simulation of control algorithms for key functionalities in a model that approaches Level 2 autonomy, utilising MATLAB Simulink and IPG CarMaker. The focus is on four critical areas: Autonomous Emergency Braking (AEB), Adaptive Cruise Control (ACC), Lane Detection (LD) and Traffic Object Detection. Also, the integration of low-level PID controllers for precise steering, braking and throttle actuation, ensures smooth and responsive vehicle behaviour. The hardware architecture is built around the Nvidia Jetson Nano and multiple Arduino Nano microcontrollers, each responsible for controlling specific actuators within the drive-by-wire system, which includes the steering, brake and throttle actuators. Communication
Ann Josy, Tessa, Sadique, Anwar, Thomas, Merlin, Manaf T M, Ashik, Vr, Sreeraj
Technical Paper
SAE TOMORROW TODAY - Matching Buyers & Sellers to Advance SDV Development1349701/16/2025
From evolving consumer expectations to changing business models to updates to the car itself, there is no doubt that software is radically transforming the automotive industry--and companies must adapt now or risk being left behind. The rise of smartphones has permanently altered consumer expectations, and software-defined vehicles (SDVs) are expected to provide experiences akin to those found on mobile devices. Preferences for infotainment and safety features through advanced driver-assistance systems (ADAS) are also increasingly central to purchase decisions. In an effort to meet these demands and accelerate the development of SDVs, SDVerse was developed as a B2B sales marketplace for buyers and sellers of automotive software. The matchmaking platform is available to all OEMs, suppliers, and other companies with relevant software offerings and tools. To learn more, we sat down with Prashant Gulati, Chief Executive Officer, to discuss the role of SDVerse in the automotive industry's
Hineman, Marcie
Podcast
Verification of Vulnerable Road User Pre-Crash Scenarios in Vision-Based Safety Applications12-08-01-000401/10/2025
Verifying training datasets in vision-based vehicle safety applications is crucial to understanding the potential limitations of detection capabilities that may result in a higher safety risk. Vision-based pedestrian safety applications with crash avoidance technologies rely on prompt detection to avoid a crash. This research aims to develop a verification process for vulnerable road user safety applications with vision-based detection functionalities. It consists of reviewing the application’s safety requirements, identifying the target objects of detection in the operational design domain and pre-crash scenarios, and evaluating the safety risks qualitatively by examining the training dataset based on the results of pre-crash scenarios classification. As a demonstration, the process is implemented using open-source pedestrian tracking software, and the pre-crash scenarios are classified based on the trajectories of pedestrians in an example training dataset used in a pedestrian
Hsu, Chung-Jen
Journal Article
SAE TOMORROW TODAY: How AI Alignment Makes AVs Safer1349501/10/2025
In order for AVs to perform safely and reliably, we need to teach them the language of human preference and expectations--and accelerating AI alignment can do just that. Enter Kognic, the industry-leading annotation platform for sensor-fusion datasets (e.g., camera, radar, and LIDAR data). By helping companies gather, organize, and refine massive datasets used for training AI models, Kognic is helping to ensure that AD/ADAS perform reliably and meet safety standards--all while minimizing costs and optimizing teams. To learn more, we sat down with Daniel Langkilde, Co-Founder and CEO, to discuss why the future of autonomous driving depends on effectively managing AI-driven datasets and how Kognic is leading dataset management for safety-critical AI. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in
Hineman, Marcie
Podcast
Vehicle Collision Avoidance Path Planning Based on Trajectory Prediction12-08-03-002901/07/2025
To address the issues of unreasonable collision avoidance path planning algorithms and inadequate safety in high-speed scenarios, a trajectory prediction-based collision avoidance path planning algorithm has been proposed. First, a trajectory prediction model is constructed using the long–short-term memory (LSTM) network, and the trajectory prediction model is trained and tested with the HighD dataset. Second, the future trajectory of the obstacle car is predicted, the future trajectory information of the two cars is combined to generate the lane-changing decision, and the three-times B-spline curves are used to generate the collision avoidance path clusters. The optimal collision avoidance paths are generated based on the multi-objective optimization function. Finally, build a MATLAB/CarSim simulation platform to verify the reasonableness and safety of the planned paths by taking the three scenarios of the continuous overtaking, preceding car pulling out, and the neighboring car
Liu, Xiao Long, Zhang, Lei, Li, Peng Kun, Xie, Ru, Wang, Qing, Li, Ran Ran
Journal Article
Cooperation Dynamics in Multiagent Systems: Modeling Vehicular Cooperation through Game Theory12-08-03-002612/24/2024
Cooperation lies at the core of multiagent systems (MAS) and multiagent reinforcement learning (MARL), where agents must navigate between individual interests and collective benefits. Advanced driver assistance systems (ADAS), like collision avoidance systems and adaptive cruise control, exemplify agents striving to optimize personal and collective outcomes in multiagent environments. The study focuses on strategies aimed at fostering cooperation with the aid of game-theoretic scenarios, particularly the iterated prisoner’s dilemma, where agents aim to optimize personal and group outcomes. Existing cooperative strategies, such as tit-for-tat and win-stay lose-shift, while effective in certain contexts, often struggle with scalability and adaptability in dynamic, large-scale environments. The research investigates these limitations and proposes modifications to align individual gains with collective rewards, addressing real-world dilemmas in distributed systems. By analyzing existing
Nidamanuri, Jaswanth, Sathi, Vaigarai, Shaik, Sabahat
Journal Article
Physical and Cognitive Driver Reactions Characterization in Response to Active Safety Systems: A Preliminary Study2024-36-013612/20/2024
Advanced Driver Assistance Systems (ADAS) are technologies that automate, facilitate, and improve the vehicle’s systems. Indeed, these systems directly interfere with braking, acceleration, and drivability of driving operations. Thus, the use of ADAS directly reflects the psychology behind driving a vehicle, which can have an automation level that varies from fully manual (Level 0) to fully autonomous (Level 5). Even though ADAS technologies provide safer driving, it is still a challenge to understand the complexity of human factors that influence and interact with these new technologies. Also, there has been limited exploration of the correlation between the physical and cognitive driver reactions and the characteristics of Brazilian roads and traffic. Therefore, the present work sought to establish a preliminary investigation into a method for evaluating the driving response profile under the influence of ADAS technologies, such as Lane Centering and Forward Collision Warning, on
Castro, Gabriel M., Silva, Rita C., Miosso, Cristiano J., Oliveira, Alessandro B. S.
Technical Paper
Lightweight Neural Network Model and Algorithm for Pedestrian Detection12-08-03-002712/18/2024
Traditional pedestrian detection methods have poor robustness. Deep learning-based methods have shown high performance in recent years but rely on substantial computational resources. Developing a lightweight, deep learning-based pedestrian detection algorithm is essential for applying deep learning-based algorithms in resource-limited scenarios, such as driverless and advanced driver assistance systems. In this article, an improved model based on YOLOv3 called “YOLOPD” (You Only Look Once—Pedestrian Detection), is proposed. It is obtained by constructing a self-attentive module, introducing a CIOU (Complete Intersection over Union) loss function and a depth separated convolutional layer. Experimental results show that on the INRIA (National Institute for Research in Computer Science and Automation), Caltech, and CityPerson pedestrian dataset, the MR (miss rate) of the model YOLOPD is better than that of the original YOLOv3 model, and the number of parameters is reduced by about 1/3
Li, Shanglin, Wang, Qi Feng, Li, Ren Fa, Xiao, Juan
Journal Article
Path Planning with Rear-Wheel Steering for Enhanced Parking Efficiency in Narrow Parking Spaces2024-01-705112/13/2024
Path planning in parking scenarios for vehicles with Ackermann steering characteristics is a well studied problem in the literature. However, the recent emergence of four-wheel steering (4WS) chassis has brought new opportunities to the field of motion planning. Compared with front-wheel steering (2WS), 4WS vehicles offer higher flexibility and new maneuver modes such as CrabWalk. To utilize such new potential to further improve parking efficiency, this paper proposes a four-wheel steering oriented planning algorithm for parking scenarios. First, Hybrid A*-4WS is proposed to search for a coarse trajectory from the starting pose to the parking slot, with improved node expansion mechanism to incorporate four-wheel steering characteristics. Then a nonlinear programming (NLP) problem is formulated with four-wheel steering kinematic model to fully utilize the maneuver capability of 4WS vehicles, with OBCA used for collision avoidance constraints. Finally, the two algorithms are sequentially
Song, Yufei, Liu, Yuanzhi, Xiong, Lu, Tang, Chen
Technical Paper
SAE Truck & Off-Highway Engineering: December 202424TOFHP1212/12/2024
Demoing clean hydrogen combustion Backed by a consortium of companies, Southwest Research Institute's demonstrator vehicle aims to prove the commercial viability of hydrogen engines for on-road trucks. Improving the ageless ICE Clean-burning fuels, aftertreatment and other innovations place the heavy-duty combustion engine on a low-carbon emissions trajectory. Making software-defined vehicles cyber-secure Virtualization features such as digital twins and virtual patching can accelerate development and make commercial vehicles more agile and secure. Editorial Bauma the big one in 2025 CATL expands battery range for heavy-duty trucks and buses Donaldson's filters aim to keep fuel cells clean Shell's Starship 3.0 NG spotlights efficient powertrain, aero tech Sensata's sensors aid blind spot monitoring for tractor-trailers MTA expands wireless communication, EV tech for trucks Komatsu employs 'slick' battery swapping for underground LHD machine Liebherr, Fortescue reveal towering battery
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