This paper focuses on the path following control problems for autonomous driving vehicles. Aiming at enhancing the robustness and attenuating the chattering phenomenon, a super-twisting sliding mode control algorithm (STA) is developed based on Lyapunov theory, where the proof of the stability of the control system is presented by applying the backstepping technique. Moreover, co-simulation between Matlab/Simulink and Carsim is carried out to verify the path following control performance. In this research, Stanley controller, conventional sliding mode control (SMC), and model predictive control (MPC) are used as the benchmark controllers for evaluating the proposed STA performance. Two driving scenarios are considered in the simulations, including normal driving and fierce driving. To comprehensively assess the control performance and control effort (i.e. magnitude of steering), an integrated and weighted performance evaluation index (IWPEI ) is novelly provided. Simulation results show that the IWPEI of the proposed STA can be reduced by 40.5%, 25.8%, 10.9% in the normal driving scenario; and 62.5%, 24%, 6.8% in the fierce driving scenario as compared with Stanley controller, conventional SMC, and MPC, respectively. The results also indicate that the proposed STA outperforms the conventional SMC in terms of the chattering attenuation, resulting in a smoother front steering wheel angle input and a smoother yaw rate performance. As compared with MPC, the advantage of the proposed STA lies in its much lower computational complexity. Furthermore, the robustness of the controllers is verified by changing the vehicle mass and tire parameters. The proposed STA can reduce the fluctuation of the IWPEI by 22.6%, 22.3%, and 5.9% compared with the benchmark approaches. These results imply that the consideration of system perturbations is very critical in the design of the super-twisting sliding mode controller which can improve the robustness of the autonomous vehicle path following system.

The latest electric vehicles (EVs) are becoming increasingly complex and resource-intensive owing to their advanced functional capabilities and autonomy. In this study, EVs are considered as cyber-physical systems (CPS), and a novel event-triggered sliding mode controller (ET-SMC) with a hierarchical framework is developed to enhance lateral stability and reduce communication resource consumption in EVs through torque vectoring control (TVC). The high-level controller of the ET-SMC is designed based on a nonlinear vehicle model, and its stability is rigorously proven using the Lyapunov and input-to-state stable (ISS) theorems. Additionally, a traditional periodic sliding mode controller (SMC) is developed as a benchmark controller. The Sine-with-Dwell (SwD) test scenario, an internationally recognized method for verifying lateral stability, is employed to evaluate the control performance using a hardware-in-the-loop (HIL) test bench. The experimental results demonstrate that the ET-SMC can not only maintain similar control performance in enhancing lateral stability but also reduce controller communication resource consumption by approximately 30% compared to the periodic SMC in this typical driving scenario. These results show that the proposed control algorithm is promising for future EVs.

A novel hierarchical direct yaw moment controller is designed to enhance the lateral stability of the four-wheel-drive electric vehicle. The adaptive sliding mode control (ASMC) technique in the upper-layer controller is employed to compute an additional yaw moment. The lower-layer controller distributes this yaw moment into each independent wheel by utilizing model predictive control allocation (MPCA). The proposed MPCA aims to mitigate the performance deterioration induced by in-wheel motor dynamics and optimize the power consumption stemming from the additional yaw moment. Co-simulation and hardware-in-the-loop (HIL) test is conducted to verify the performance of the proposed controller. Validation results show that the proposed hierarchical ASMC-MPCA controller outperforms the sliding mode control MPCA (SMC-MPCA) and the integrated nonlinear model predictive control (NMPC) with the lowest root-mean-square errors [ ] of yaw rate, sideslip angle, lateral deviation, and lowest power consumption. Additionally, the chattering phenomenon in SMC-MPCA can be suppressed effectively by adaptively estimating the parameter uncertainties. The proposed ASMC-MPCA controller also consumes less computational resources than the NMPC and SMC-MPCA, which indicates that the ASMC-MPCA is more suitable for an automotive onboard controller. The comparison between hierarchical and integrated controller frameworks also shows that the hierarchical framework is more suitable for production vehicles under non-powerful vehicle control units.

In terms of the possible secondary or multiple events accidents (MEA) after an initial vehicle to vehicle (V2V) impact, this research proposes an active safety & stability controller for independent‐driven electric vehicles towards recovering the vehicle to safety states after an impact. The controller aims to regulate the course angle and lateral deviation that enforce the vehicle back to its original driving path. The upper‐level controller is derived based on sliding mode technique. And the low‐level controller aims to solve a convex quadratic allocation problem, which inherently incorporates fault‐tolerance property. The independent in‐wheel‐motor (IWM) fault is very likely to happen under a real V2V impact and it contributes much to the driving safety & stability. Two typical real‐end‐collision scenarios under low‐ and high longitudinal velocities are designed to verify the proposed controller performance. The low‐ and high velocities collisions aim to simulate the urban and highway accidents, respectively. Compared to other control methods, i.e. pure braking and static allocation, the RMSEs of safety (lateral deviation, course angle) and stability indexes (yaw rate, sideslip angle) under proposed controller reduce significantly both in urban and highway accidents. Moreover, the controller performs robust enough against the impact‐induced IWM fault. It further supports the effectiveness at dealing with the safety and stability of the ego vehicle after an initial impact and prevent possible MEA.

Reliable cooperative control of connected vehicle platoons remains challenging when stochastic communication-topology variations coexist with heterogeneous time-varying transmission delays. This study investigates a distributed consensus control problem for connected vehicle platoons subject to Markovian switching topologies and heterogeneous time-varying inter-vehicle communication delays. A third-order longitudinal vehicle model with powertrain inertial lag is combined with a continuous-time Markov chain (CTMC) to describe randomly switching communication topologies. A distributed control protocol incorporating the constant time headway policy, full velocity difference car-following behavior, and leader-pinning information is formulated, while link-dependent time-varying delays are embedded in the closed-loop error system. Based on a mode-dependent Lyapunov–Krasovskii functional, Jensen’s integral inequality, and the Schur complement, sufficient linear matrix inequality (LMI) conditions are derived for mean-square asymptotic stability of the nominal delayed system; the LMI is feasible at ${\overline{\tau }}_{\mathsf{\ell }}=0.10$ s and ${\mu }_{\mathsf{\ell }}=0.04$, remains feasible at $0.30$ s, and becomes infeasible at $0.375$ s. An input-to-state stability (ISS) corollary extends the result to bounded time-varying leader acceleration, showing that tracking errors remain bounded during dynamic EUDC phases and converge when the leader acceleration and its derivative vanish. Using the same Lyapunov–Krasovskii certificate, a mean-square $L_{\infty }$ bounded-propagation result is further established; this result characterizes uniform-in-vehicle error bounds and does not assert strict non-amplification. Numerical simulations under the Extra-Urban Driving Cycle (EUDC) show that the follower vehicles maintain cooperative tracking under topology switching and bounded communication delays, with position, velocity, and acceleration errors remaining bounded during the dynamic phases and converging as the leader acceleration vanishes. In the EUDC simulation, the largest leader-relative position error is approximately $-60.75$ m for Vehicle 10 at $t\approx 380$ s, the maximum adjacent local spacing-error peak ratio is $2.32$, and the position error returns below 1 m by $t\approx 400.8$ s. The minimum inter-vehicle gap is $5.87$ m and the minimum safety margin after the initial 10 s is $0.84$, with all safety-margin indices remaining positive; these results indicate that the proposed framework maintains platoon coordination and car-following safety under stochastic communication uncertainties.

Accurate estimation of motion sickness incidence (MSI) is important for vehicle comfort optimization and motion-sickness mitigation. Conventional vibration-based functional models generally use vehicle acceleration directly and therefore do not account for mechanical transmission between the vehicle and the occupant. This study develops axis-decoupled four-degree-of-freedom (four-DOF) models of the effective vehicle-floor-to-back vibration transmission of seated occupants. The model-reconstructed back/upper-torso acceleration is used as a configuration-specific exposure descriptor in a functional MSI estimation model that accounts for frequency weighting and temporal accumulation. Real-vehicle experiments showed that the coefficients of determination R 2 between the model-predicted and experimentally measured magnitudes of effective floor-to-back acceleration transmissibility were 0.9648, 0.9665, and 0.9524 in the z-, y-, and x-directions, respectively. These results indicate that the four-DOF models closely reproduced the experimentally observed magnitude characteristics of vibration transmission, supporting their subsequent use in MSI estimation. Under straight and slalom driving, the back-response-based estimation model reduced the group-level root mean square error (RMSE) by 22.10% and 17.54%, respectively, compared with the direct model. Additional analyses examined estimation performance across sex and body-mass groups, naturalistic smartphone viewing, and input perturbations.