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The current focus in the ongoing development of autonomous driving systems (ADS) for heavy duty vehicles is that of vehicle operational safety. To this end, developers and researchers alike are working towards a complete understanding of the operating environments and conditions that autonomous vehicles are subject to during their mission. This understanding is critical to the testing and validation phases of the development of autonomous vehicles and allows for the identification of both the nominal and edge case scenarios encountered by these systems. Previous work by the authors saw the development of a comprehensive scenario generation framework to identify an operating domain specification (ODS), or external and internal conditions an autonomous driving system can expect to encounter on its mission to form critical scenario groups for autonomous vehicle testing and validating using statistical patterns, clustering, and correlation.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

Lane changing is an essential action in commercial vehicles to prevent collisions. However, steering system malfunctions significantly escalate the risk of head-on collisions. With the advancement of intelligent chassis control technologies, some autonomous commercial vehicles are now equipped with a four-wheel independent braking system. This article develops a lane-changing control strategy during steering failures using torque vectoring through brake allocation. The boundaries of lane-changing capabilities under different speeds via brake allocation are also investigated, offering valuable insights for driving safety during emergency evasions when the steering system fails. Firstly, a dual-track vehicle dynamics model is established, considering the non-linearity of the tires. A quintic polynomial approach is employed for lane-changing trajectory planning. Secondly, a hierarchical controller is designed.

The strategy for emission reduction in the P2.5 hybrid system involves the optimization of engine torque, engine speed, catalyst heat duration, and motor torque regulation in a coordinated manner. In addition to employing traditional engine control methods used in HEV models, unique approaches can be utilized to effectively manage emissions. The primary principle is to ensure that the engine operates predominantly under steady-state conditions or limits its load to regulate emissions levels. The main contributions of this paper are as follows: The first is the optimization of catalyst heating stage. During the catalyst heating stage, the system divides it into one or two stages. In the first stage, the vehicle is driven by the motor while keeping the engine idle. This approach stabilizes catalyst heating and prevents fluctuations in air-fuel ratio caused by speed and load changes that could potentially worsen emissions performance.

The collision accidents of electric vehicles are gradually increasing, and the response of battery cell under mechanical abuse conditions has attracted more and more attention. In the real collision, the mechanical load on battery generally has the following characteristics, including multiple loading directions, dynamic impact and blunt intrusion. Therefore, it is necessary to study the mechanical response and deformation of battery under complex loading, especially in-plane dynamic loading condition. According to the actual accident, we designed the constrained blunt compression test of the battery in different speeds and directions. For out-of-plane loading, the structural stiffness of battery increases obviously and the fracture is advanced compared with the corresponding quasi-static tests. For in-plane constrained loading, the force response can be approximately divided into two linear segments, in which the structural stiffness increases abruptly after the inflection point.

Adoption of fuel cell electric vehicles (FCEV) or battery electric vehicles (BEV) in heavy-duty (HD) commercial freight transportation is hampered by difficult technoeconomic obstacles. To enable widespread deployment of electrified powertrains, fleet and operational logistics need high uptime and parity with diesel system productivity/total cost of ownership (TCO), while meeting safety compliance. Due to a mix of comparatively high powerplant and energy storage costs, high energy costs (more so for FCEV), greater weight (more so for BEV), slow refueling / recharging durations, and limited supporting infrastructure, FCEV and BEV powertrains have not seen significant uptake in the HD freight transport market. The use of dynamic wireless power transfer (DWPT) systems, consisting of inductive electrical coils on the vehicle and power source transmitting coils embedded in the roadways, may address several of these challenges.

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions.

With the rapid advancement in intelligent vehicle technologies, comprehensive environmental perception has become crucial for achieving higher levels of autonomous driving. Among various perception tasks, monitoring road types and evenness is particularly significant. Different road categories imply varied surface adhesion coefficients, and the evenness of the road reflects distinct physical properties of the road surface. This paper introduces a two-stage road perception framework. Initially, the framework undergoes pre-training on a large annotated drivable area dataset, acquiring a set of pre-trained parameters with robust generalization capabilities, thereby endowing the model with the ability to locate road areas in complex regions.

Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors.

In order to realize the series-parallel switching control of hybrid electric vehicle (HEV) with dual-motor hybrid configuration, a method of unpowered interrupt switching based on the coordinated control of three power sources was proposed by analyzing the series-parallel driving mode of the dual-motor hybrid configuration. The series to parallel switching process is divided into three stages: speed regulation stage, clutch combination and power source switching. The distribution control of speed regulating torque is carried out in the speed regulating stage. The speed adjustment torque is preferentially allocated to the power source of the input shaft (engine and P1) to carry out the lifting torque. Due to the high speed adjustment accuracy and fast response of the P1 motor, the input shaft is preferentially allocated to P1 for speed adjustment, that is, the torque intervention of P1.

In order to realize the shift control of dual-motor hybrid electric vehicle (HEV), a non-power interruption shift control method based on three-power source coordination control was proposed by analyzing the shift process of dual-motor hybrid configuration. The shift control process was divided into three stages: oil-filling self-learning stage, torque exchange stage and inertia control stage. In the torque exchange stage, the characteristics of the speed stage and torque stage were analyzed, which was different from the traditional method's dependence on pressure sensor, longitudinal acceleration sensor and engine torque accuracy. A shift clutch gain self-learning strategy based on shift time and input shaft speed soaring problem was proposed.

In order to achieve seamless mode switching control for hybrid electric vehicles (HEVs) in the event of battery failure, we propose a motor voltage-controlled mode switching method that eliminates power interruptions. This approach is based on an analysis of the dual-motor hybrid configuration's mode switching. We analyze the overall vehicle operation when the high-voltage battery occurs in different hybrid modes. To ensure that the vehicle can still function like a conventional car under such circumstances, we introduce a novel "voltage control" mode. In this mode, instead of operating in its traditional torque control manner, the P1 motor adopts a voltage control strategy. The P1 controller's variable becomes "voltage," and VCU sends the motor's working mode switching request and PCM finishes the mode transition. During system operation, the P1 motor promptly responds to these target voltages to maintain bus voltage within a normal range.

In order to solve the problems of the shuffle caused by internal and external excitation and the difficulty in obtaining the real-time accurate engine torque during the parallel mode operation of hybrid electric vehicles, a dynamic coordination control strategy for suppressing the jitter of hybrid electric vehicles based on the closed-loop control of engine speed was proposed. The engine torque filtering control method based on the slope limit was adopted to limit the rate of change of the engine torque and reduce the impact caused by the sudden change of the engine torque; the engine speed closed-loop control method was used to take the motor speed which is easy to be measured accurately in real time as the feedback control variable, which solved the problem of the real-time accurate estimation of the engine torque online. In parallel mode, the motor torque accounts for a small proportion because the torque distribution method gives priority to the engine.

In hybrid vehicles, the drive motor is directly connected to the drive train and the inherent drive train damping is low. When subjected to external disturbance, the low damping characteristics of the transmission system may cause torsional vibration, which will continue to oscillate the transmission system and affect the driving performance of the vehicle. In this paper, we propose a harmonic injection wheel control method based on motor speed to suppress oscillations and improve the driving performance of hybrid electric vehicles. The harmonic injection control method based on motor speed is based on Fourier transform to decompose sinusoidal harmonics based on specific order of motor speed. RLS algorithm is used to estimate the amplitude and phase, and PI control is used to calculate the compensation torque for the actual amplitude and target amplitude. Simulation and test results show that the proposed control strategy is effective in suppressing oscillations.

The phenomena of global warming and climate change are encouraging more and more countries, local communities, and companies to establish carbon neutrality targets, which has very significant implications for the US trucking industry. Truck electrification helps fleets to achieve zero tailpipe emissions and macro-scale decarbonization while allowing continued business growth in response to the rapid expansion of e-commerce and shipping related to increased globalization. This paper presents an analysis of Class 8 long-haul truck electrification using a commercial vehicle electrification evaluation tool and Fleet DNA drive data. The study provides new insight into the impacts of streamlined chassis, battery energy density, and superfast charging on battery capacity needs as well as implications for payload, energy consumption, and greenhouse gas emissions for electric long-haul trucks. The study also identifies a pathway for achieving optimal long-haul truck electrification.

Reducing criteria pollutants while reducing greenhouse gases is an active area of research for commercial on-road vehicles as well as for off-road machines. The heavy duty on-road sector has moved to reducing NOx by 82.5% compared to 2010 regulations while increasing the engine useful life from 435,000 to 650,000 miles by 2027 in the United States (US). An additional certification cycle, the Low Load Cycle (LLC), has been added focusing on part load operation having tight NOx emissions levels. In addition to NOx, the total CO2 emissions from the vehicle will also be reduced for various model years. The off-road market is following with a 90% NOx reduction target compared to Tier 4 Final for 130-560 kW engines along with greenhouse gas targets that are still being established. The off-road market will also need to certify with a Low Load Application Cycle (LLAC), a version of which was proposed for evaluation in 2021.

Due to the objectives of achieving high fuel efficiency and drivability performance, a dual-drive hybrid system with two motors has been developed. Various drive modes are presented based on engine status, requested driver torque and power, as well as C0 status in different working conditions. The transition control of drive mode change poses a unique challenge for the dual-drive hybrid system. This study discusses the control strategies for transitioning between drive modes. The first type of transition mode is divided into four distinct phases. In the second mode transition, there are three phases: the synchronization phase involving P1 torque intervention, the C0 lock-up phase involving frozen P1 torque control and adjustment of C0 clutch torque and pressure correlation, and finally, the torque exchange phase. The third type of transition includes a dedicated torque transition phase followed by a C0 disengaged phase and concluding with a speed synchronization phase.

The present study was motivated by a need to expand information for consumers offered through the FuelEconomy.Gov website. To that end, a power-based modeling approach has been used to examine the effect of steady-speed driving on estimated range for model year 2020 – 2023 battery electric vehicles (BEVs). This approach allowed rapid study of a broader range of BEV models than could be accomplished through vehicle tests. Publicly accessible certification test results and other data were used to perform a regression between cycle-average tractive power requirements and the resulting electrical power. This regression enabled estimation of electric power and energy use over a range of steady highway speeds. These analyses in turn allowed projection of vehicle range at differing speeds. The projections agree within 6% with available 65 MPH manufacturer test data.

This paper defines a control method for shift torque exchange stage and a torque distribution control method for speed regulation stage. In the torque exchange stage, the torque distribution problem of active and passive clutches considers the injection of sine curve for local correction, which can solve the fish belly problem of hydraulic response (i.e. the hydraulic response is slow at the beginning and the hydraulic response is fast at the end). In the speed regulation stage, the target speed gradient profile is determined according to different shift types. The determination of the target speed gradient profile integrates different driving modes, throttle, P2 energy and clutch temperature.

The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model.