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This paper details the advancements and outcomes of the NEXTCAR (Next-Generation Energy Technologies for Connected and Automated on-Road Vehicles) program, an initiative led by the Advanced Research Projects Agency-Energy (ARPA-E). The program focusses on harnessing the full potential of Connected and Automated Vehicle (CAV) technologies to develop advanced vehicle dynamic and powertrain control technologies (VD&PT). These technologies have shown the capability to reduce energy consumption by 20% in conventional and hybrid electric cars and trucks at automation levels L1-L3 and by 30% L4 fully autonomous vehicles. Such reductions could lead to significant energy savings across the entire U.S. vehicle fleet.

With the trend of electrification and connectivity, more electrified parts and more integrated chips are being applied. Consequently, potential problems based on electro-magnetic could occur more easily, and interest on EMC performance has been rising according to the degree of electrification. In this paper, one of the most severe systems, cooling fan motor in terms of EMI, is analyzed and improvement methods are suggested for each type of cooling fan. Additionally, an optimized configuration of improvement method for EMC has been derived through analysis and study. Finally, verification and validation are implemented at the system and vehicle levels.

To develop safe vehicles, system development must be performed in compliance with functional safety. Functional safety considers situations where failures could make a vehicle unsafe, and it requires the inclusion of mechanisms to detect and mitigate these failures, even though they may not always be detected with 100% certainty — referred as diagnostic coverage (DC). Therefore, some faults, called residual faults, might go undetected. In the realm of functional safety from a communication perspective, industry standards define nine distinct fault modes. The detection of these faults is crucial, especially in the widely used AUTOSAR automotive operating system. AUTOSAR E2E (End-to-End Communication Protection) serves as a communication fault detection mechanism utilizing three mechanisms: counters, timers, and Cyclic Redundancy Check (CRC) to address the nine fault modes. Especially, determining the DC for CRC can be challenging and often requires a conservative evaluation approach.

SAE J1979 and its “OBD Modes” served for the protection of our environment against harmful pollutants for decades, but due to regulatory adoption of Unified Diagnostic Services (UDS), SAE J1979 has now become a multiple part document series: SAE J1979 will be replaced by SAE J1979-2 for vehicles with combustion engines (ICEs) and by SAE J1979-3 for Zero Emission Vehicle (ZEV) propulsion systems. For ZEVs, emission-related failures will be replaced by ZEV propulsion-related failures. Both SAE J1979-2 and -3 are variants of ISO 14229 (UDS) but limited to emission-related and ZEV propulsion-related failures, respectively, and associated diagnostic data. These new diagnostic communication protocols are required by California Air Resources Board (CARB) but do not support vehicle-manufacturer-specific diagnostic applications like calibration or flash programming.

The current automotive industry has a growing demand for real-time transmission to support reliable communication and for key technologies. The Time-Sensitive Networking (TSN) working group introduced standards for reliable communication in time-critical systems, including shaping mechanisms for bounded transmission latency. Among these shaping mechanisms, Cyclic Queuing and Forwarding (CQF) and frame preemption provide deterministic guarantees for frame transmission. However, despite some current studies on the performance analysis of CQF and frame preemption, they also need to consider the potential effects of their combined usage on frame transmission. Furthermore, there is a need for more research that addresses the impact of parameter configuration on frame transmission under different situations and shaping mechanisms, especially in the case of mechanism combination.

The advent of Vehicle-to-Everything (V2X) communication has revolutionized the automotive industry, particularly with the rise of Advanced Driver Assistance Systems (ADAS). V2X enables vehicles to communicate not only with each other (V2V) but also with infrastructure (V2I) and pedestrians (V2P), enhancing road safety and efficiency. ADAS, which includes features like adaptive cruise control and automatic intersection navigation, relies on V2X data exchange to make real-time decisions and improve driver assistance capabilities. Over the years, the progress of V2X technology has been marked by standardization efforts, increased deployment, and a growing ecosystem of connected vehicles, paving the way for safer and more efficient automated navigation. The EcoCAR Mobility Challenge was a 4-year student competition among 12 universities across the United States and Canada sponsored by the U.S.

Autonomous Vehicles are being widely tested under diverse conditions with expectations that they will soon be a regular feature on roads. The development of Autonomous Vehicles has become an important policy in countries around the world, and the technologies developed by countries and car manufacturers are different, and at the same time to adapt to the road environment and traffic management facilities of different countries, so some countries have built self-driving test sites, and the test content is also different, so it is impossible to compare its relative difficulty. This study surveyed experts and scholars to develop a means of weighting the respective difficulty of various autonomous vehicle testing conditions based on the analytic hierarchy process and fuzzy analytic hierarchy process, applied to a sample of 33 sets of testing conditions based on road type, management actions and operational capabilities.

Over the past decade, significant progress has been made in developing algorithms and improving hardware for automated driving. However, conducting research and deploying advanced algorithms on automated vehicles for testing and validation remains costly, especially for academia. This paper presents the efforts of our research team to integrate the newest version of the open-source Autoware software with the commercially available DataSpeed Drive-by-Wire (DBW) system, resulting in the creation of a versatile and robust automated vehicle research platform. Autoware, an open-source software stack based on the 2nd generation Robot Operating System (ROS2), has gained prominence in the automated vehicle research community for its comprehensive suite of perception, planning, and control modules. The DataSpeed DBW system directly communicates with the vehicle's CAN bus and provides precise vehicle control capabilities.

The SAE AutoDrive Challenge II is a four-year collegiate competition dedicated to developing a Level 4 autonomous vehicle by 2025. In January 2023, the participating teams each received a Chevy Bolt EUV. Within a span of five months, the second phase of the competition took place in Ann Arbor, MI. The authors of this contribution, who participated in this event as team Wisconsin Autonomous representing the University of Wisconsin–Madison, secured second place in static events and third place in dynamic events. This has been accomplished by reducing reliance on the actual vehicle platform and instead leveraging physical analogs and simulation. This paper outlines the software and hardware infrastructure of the competing vehicle, touching on issues pertaining sensors, hardware, and the software architecture employed on the autonomous vehicle. We discuss the LiDAR-camera fusion approach for object detection and the three-tier route planning and following systems.

An autonomous vehicle is a comprehensive intelligent system that includes environment sensing, vehicle localization, path planning and decision-making control, of which environment sensing technology is a prerequisite for realizing autonomous driving. In the early days, vehicles sensed the surrounding environment through sensors such as cameras, radar, and lidar. With the development of 5G technology and the Vehicle-to-everything (V2X), other information from the roadside can also be received by vehicles. Such as traffic jam ahead, construction road occupation, school area, current traffic density, crowd density, etc. Such information can help the autonomous driving system understand the current driving environment more clearly. Vehicles are no longer limited to areas that can be sensed by sensors. Vehicles with different autonomous driving levels have different adaptability to the environment.

Automatic emergency braking and forward collision warning (FCW) reduce the incidence of police-reported rear-end crashes by 27% to 50%, but these systems may not be effective for preventing rear-end crashes with nonpassenger vehicles. IIHS and Transport Canada evaluated FCW performance with 12 nonpassenger and 7 passenger vehicle or surrogate vehicle targets in five 2021-2022 model year vehicles. The presence and timing of an FCW was measured as a test vehicle traveling 50, 60, or 70 km/h approached a stationary target ahead in the lane center. Equivalence testing was used to evaluate whether the proportion of trials with an FCW (within ± 0.20) and the average time-to-collision of the warning (within ± 0.23 sec) for each target was meaningfully different from a global vehicle car target (GVT).

Most of the Automated Driving Systems (ADS) technology development is targeting urban areas; there is still much to learn about how ADS will impact rural transportation. The DriveOhio team deployed level-3 ADS-equipped prototype vehicles in rural Ohio with the goal of discovering technical challenges for ADS deployment in such environments. However, before the deployment on public roads, it was essential to test the ADS-equipped vehicle for their safety limitations. At Transportation Research Center Inc. (TRC Inc.) proving grounds, we tested one such prototype system on a closed test track with soft targets and robotic platforms as surrogates for other road users. This paper presents an approach to safely conduct testing for ADS prototype and assess its readiness for public road deployment. The main goal of this testing was to identify a safe Operational Design Domain (ODD) of this system by gaining better understanding of the limitations of the system.

Robustness testing of Advanced Driver Assistance Systems (ADAS) features is a crucial step in ensuring the safety and reliability of these systems. ADAS features include technologies like adaptive cruise control, lateral and longitudinal controls, automatic emergency braking, and more. These systems rely on various sensors, cameras, radar, lidar, and software algorithms to function effectively. Robustness testing aims to identify potential vulnerabilities and weaknesses in these systems under different conditions, ensuring they can handle unexpected scenarios and maintain their performance. Mileage accumulation is one of the validation methods for achieving robustness. It involves subjecting the systems to a wide variety of real-world driving conditions and driving scenarios to ensure the reliability, safety, and effectiveness of the ADAS features.

The knowledge of representative load collectives and duty cycles is crucial for designing and dimensioning vehicles and their components. For human driven vehicles, various methods are known for deriving these load spectra directly or indirectly from fleet measurement data of the customer vehicle operation. Due to the lack of market penetration of highly automated and autonomous vehicles, there is no sufficient fleet data available to utilize these methods. As a result of increased demand for ride comfort compared to human driven vehicles, autonomous vehicle operation promises reduced driving speeds as well as reduced lateral and longitudinal accelerations. This can consequently lead to decreasing operation loads, thus enabling potentially more light-weight, cost-effective, resource-saving and energy-efficient vehicle components.

Digital twin technology has become impactful in Industry 4.0 as it enables engineers to design, simulate, and analyze complex systems and products. As a result of the synergy between physical and virtual realms, innovation in the “real twin” or actual product is more effectively fostered. The availability of verified computer models that describe the target system is important for realistic simulations that provide operating behaviors that can be leveraged for future design studies or predictive maintenance algorithms. In this paper, a digital twin is created for an offroad tracked vehicle that can operate in either autonomous or remote-control modes. Mathematical models are presented and implemented to describe the twin track and vehicle chassis governing dynamics. These components are interfaced through the nonlinear suspension elements and distributed bogies.

Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on.

Compared to traditional vehicles, four-wheel independent drive and four-wheel independent steering (4WID-4WIS) vehicles have gained significant attention from researchers due to their enhanced control flexibility and superior handling performance. The steering angle deviation caused by dynamic toe angle changes in two-wheel steering (2WS) systems is often minimal and hence overlooked. However, the impact becomes notably significant in 4WIS systems. This article contrasts the tire slip angle differences between 2WS and 4WIS, and delves into the effects of dynamic toe angle variations on 4WIS control. Solutions are proposed both in terms of steering angle control and suspension design. Firstly, a dynamic model for the 4WID-4WIS vehicle is established. Secondly, a hierarchical tire force distribution strategy is designed for trajectory tracking.

Vehicle yaw stability control (YSC) can actively adjust the working state of the chassis actuator to generate a certain additional yaw moment for the vehicle, which effectively helps the vehicle maintain good driving quality under strong transient conditions such as high-speed turning and continuous lane change. However, the traditional YSC pursues too much driving stability after activation, ignoring the difference of multi-objective requirements of yaw maneuverability, actuator energy consumption and other requirements in different vehicle stability states, resulting in the decline of vehicle driving quality. Therefore, a vehicle yaw stability model predictive control strategy for dynamic and multi-objective requirements is proposed in this paper. Firstly, the unstable characteristics of vehicle motion are analyzed, and the nonlinear two-degree-of-freedom vehicle dynamics models are established respectively.

Electro-Mechanical Braking (EMB) system, which completely abandons the traditional hydraulic device, realizes complete human-vehicle decoupling and integrates various functions without adding additional accessories, could meet the requirements of the future intelligent driving technology for high-quality braking control. However, there are significant internal interference of nonlinear characteristics such as mechanical friction and system variable stiffness during the actual working process of EMB, and these make the accuracy and rate of the clamping force control decline. This paper proposes a precise clamping force control strategy for EMB based on nonlinear characteristics compensation. First, we systematically analyze the working principle of EMB, and establish the mathematical model of EMB system including motor, transmission mechanism and friction. At the same time, some typical experiments are designed to identify internal parameters of friction model.

Brake pulsation is a low frequency vibration phenomenon in brake judder. In this study, a simulation approach has been developed to understand the physics behind brake pulsation employing a full vehicle dynamics CAE model. The full vehicle dynamic model was further studied to understand the impact of suspension tuning variation to brake pulsation performance. Brake torque variation (BTV) due to brake thickness variation from uneven rotor wear was represented mathematically in a sinusoidal form. The wheel assembly vibration from the brake torque variation is transmitted to driver interface points such as the seat track and the steering wheel. The steering wheel lateral acceleration at the 12 o’clock position, driver seat acceleration, and spindle fore-aft acceleration were reviewed to explore the physics of brake pulsation. It was found that the phase angle between the left and right brake torque generated a huge variation in brake pulsation performance.