To enhance the active safety and realize the autonomy of intelligent vehicle on highway curved road, a lane changing trajectory is planned and tracked for lane changing maneuver on curved road. The kinematics model of the intelligent vehicle with nonholonomic constraint feature and the tracking error model are established firstly. The longitudinal and lateral coupling and the difference of curvature radius between the outside and inside lane are taken into account, which is helpful to enhance the authenticity of desired lane changing trajectory on curved road. Then the trajectory tracking controller of closed‐loop control structure is derived using integral backstepping method to construct a new virtual variable. The Lyapunov theory is applied to analyze the stability of the proposed tracking controller. Simulation results demonstrate that this controller can guarantee the convergences of both the relative position tracking errors and the position tracking synchronization.

In this paper, with the aim of meeting the requirements of car following, safety, comfort, and economy for adaptive cruise control (ACC) system, an ACC algorithm based on model predictive control (MPC) using constraints softening is proposed. A higher-order kinematics model is established based on the mutual longitudinal kinematics between the host vehicle and the preceding vehicle that considers the changing characteristics of the inter-distance, relative velocity, acceleration, and jerk of the host vehicle. Performance indexes are adopted to represent the multi-objective demands and constraints of the ACC system. To avoid the solution becoming unfeasible because of the overlarge feedback correction, the constraint softening method was introduced to improve robustness. Finally, the proposed ACC method is verified in typical car-following scenarios. Through comparisons and case studies, the proposed method can improve the robustness and control precision of the ACC system, while satisfying the demands of safety, comfort, and economy.

Drivers are allowed to perform a nondriving-related task (NDRT) in the Level-3 (L3) automated driving, which inevitably promotes a variety of complex NDRTs. Hence, investigating the effect of NDRTs on drivers’ behavior is necessary to ensure a safe transition from automated to manual driving. The main aim of this study is to examine the effect of resource demands of NDRTs and its engagement time on drivers’ physiological responses, takeover performance, and subjective ratings. A total of 42 participants were recruited to conduct a driving simulator study. Results showed that visual and physical resource demands significantly increased participants’ response time and deteriorated their takeover quality. Engagement time significantly affected participants’ drowsiness and response time to the takeover request. In addition, participants rated the physical–visual–cognitive task as the most challenging task and the cognitive task as the least demanding task, which matches the takeover performance. Moreover, pupil diameter responds significantly to visual resource demands rather than physical resource demands. Physical resource demands had a significant tendency to elevate heart rate. This study suggested that tasks with a certain level of cognitive resource demand may be more appropriate for L3 automated driving. The findings of this study make it possible to use physiological responses to identify NDRT types or even predict drivers’ takeover performance in automated driving. Based on this, the time budget could be dynamically adjusted to ensure a safe transition.

High-speed and complex road conditions make it easy for vehicles to reach limit conditions, increasing the risk of instability. Consequently, there is an urgent need to solve the problem of vehicle stability and safety. In this paper, adaptive stability control is studied in BEVs driven by in-wheel motors. Based on the sliding model algorithm, a joint weighting control of the yaw rate and sideslip angle is carried out, and a weight coefficient is designed using a fuzzy algorithm to realize adaptive direct yaw moment control. Next, optimal torque distribution is designed with the minimum sum of four tire load rates as the optimization objective. Then, combined with the road adhesion coefficient and the maximum motor torque constraint, the torque distribution problem is transformed into a functionally optimal solution problem with constraints. The simulation results show that the direct yaw moment controller based on the adaptive sliding mode algorithm has a good control effect on the yaw rate and sideslip angle, and it can effectively improve vehicle adaptive stability control. In the optimal torque distributor based on road surface recognition, the estimated error of road adhesion is within 10%, and has a greater margin to deal with vehicle instability, which can effectively improve vehicle adaptive stability control.

Level-2 (L2) driving automation still has many limitations, and drivers are responsible for L2 automation safety. This study investigates the impact of the criticality of takeover situations caused by longitudinal or lateral control system failures (critical, non-critical), the failure form (silent, alert), and non-driving related tasks (NDRTs) (no task, video tasks) on drivers’ physiological responses, visual behavior, and takeover performance. Sixty-eight participants completed experiments on a motion-based simulator. Results showed that participants were more challenged to recognize faded lane lines than stationary obstacles, resulting in higher accident rates. The failure form had limited effects on participants’ physiological responses and visual behavior, but the takeover request could guide participants to recognize takeover events earlier. Participants engaged in NDRTs were also responsible for monitoring the driving environment for safety. This dual task increased their visual workload and reduced their situation awareness during automation. Additionally, distracted drivers were more difficult to detect takeover events, which worsened takeover performance and increased accident rates. Specifically, 12.5% of distracted participants were in accidents, while 6.6% of those not engaged in NDRT. In addition, this study classified distracted participants into low, medium, and high-risk groups based on their takeover performance and examined the monitoring strategy and maximum eye-off-road time (max EORT). Results suggested that monitoring frequency and max EORT are more suitable for evaluating and classifying driver monitoring behaviors in L2 driving automation. The max EORT for the three groups were 6.2 s, 9.4 s, and 13.2 s, respectively. The findings of the study provide an important reference for engineers to design L2 automation systems and driver monitoring systems to assess distracted driver risk or predict their takeover responses.