Integrating intermittent behavioral characteristics into driver steering model helps to improve the human-like degree of test background vehicles in virtual simulation testing. Although the “act-and-wait” control can reproduce the intermittent steering behavior characteristic of human drivers, the process of state switch between “act” and “wait” is prone to system oscillation. To better reproduce the intermittent behavioral characteristics of human drivers, this article established a novel driver steering model in lane-keeping scenarios based on hysteresis quantization control. Additionally, a “gap” module is designed with reference to the backlash design of mechanical transmission system, which effectively eliminates the high-frequency, low-amplitude jitter during the quantization of the steering wheel angle. Then, the control condition of the system with stability is obtained by using Lyapunov’s analysis method. Finally, lane-keeping scenarios for S-turn and variable-curvature road are constructed on the Prescan platform, and the lane-keeping performance of different driver steering models is compared and analyzed under various vehicle speed conditions. The experimental results show that the driver steering model proposed in this article can reproduce the intermittent steering behavioral characteristics of human drivers well in terms of steering wheel angle, lateral deviation, yaw rate, and yaw acceleration.
Traffic oscillations degrade the operational efficiency and safety of vehicular platoons. This study proposes a predecessor-following platoon with periodically assigned feedback gains and investigates its frequency-selective disturbance attenuation mechanism. An auxiliary second-order undamped periodic model is first established, and the Bloch–Floquet formulation is used to derive the nominal dispersion relation and bandgap boundaries. Closed-loop disturbance attenuation is then evaluated separately using spacing-error transfer matrices that retain positive velocity feedback and actuator-response lag. Frequency-domain transmission analysis and time-domain simulations show that, under nominal homogeneous and fully controllable connected and automated vehicle (CAV) assumptions, the periodic gain configuration creates tunable attenuation bands and provides stronger downstream attenuation than a homogeneous-gain configuration. The results clarify the relationship among periodic gain assignment, bandgap formation, periodic-cell string stability, and finite-length traffic-wave suppression. The effects of vehicle heterogeneity, sensing delay, and mixed traffic remain topics for future investigation.
Proactive safety and vehicle automation typically requires high energy use from sensors and energy-intensive computing from sensor data processing. For high-quality, reliable perception and localization within a driving environment at the vehicle level, incoming data from multiple sensors need to be fused using advanced computational algorithms, which demand a high compute load. Alternatively, computational offloading of automated driving tasks shifts energy consumption from the vehicle to cloud infrastructure, where renewable energy sources, such as hydropower or solar power, can be utilized more efficiently. Herein, an optimal scheduling strategy for autonomous driving tasks via the cloud layer is formulated as a mixed-integer linear programming (MILP) problem and verified using measurement-informed task graphs. It is shown that cloud-based computational offloading enables energy-efficient operation while maintaining task deadlines to ensure the timely availability of perception and localization information, which is consistent with prior studies in the literature. Simulation results demonstrate that on average, 35.78% of the computational load was offloaded to the cloud, which can achieve significant energy savings for the onboard system. The compute operations achieved an average energy savings of 35.65%, while total system savings ranged from 26.16% to 30.67% under different cloud energy efficiency scenarios, highlighting the advantages of offloading compute-intensive tasks. The framework was further evaluated using multiple directed acyclic graph (DAG) configurations to assess its scalability and adaptability. Critical tasks, such as sensor fusion, were executed exclusively on the vehicle to ensure real-time responsiveness.
This article presents a hybrid battery management system for unmanned surface vehicles, integrating three operational modes: solar-to-cell balancing, capacitive balancing, and solar-to-pack charging—within a shared architecture. The system prioritizes solar-to-cell balancing when solar energy is available, capacitive balancing when charging without solar input, and solar-to-pack charging when cells are balanced. This approach eliminates additional hardware by utilizing a shared-hardware architecture. A 14.8 V battery pack composed of four 3.7 V Li-ion modules was simulated and prototyped to validate the system. Experimental and simulation results confirm improved cell balancing, reduced state-of-health deviation, and extended operational time, demonstrating superior performance, sustainability, and adaptability over conventional battery management system solutions for unmanned surface vehicles and similar platforms.
Accurate vehicle trajectory prediction remains critical for autonomous driving systems. However, accurately representing interaction-rich and time-varying behaviors is still challenging for many existing predictors, often resulting in reduced predictive accuracy. To address these limitations, we propose a kinematics-constrained Transformer with spatiotemporal repulsive force. First, we model interactions between the target vehicle and its surroundings using a spatiotemporal repulsive force model, enhancing the network’s environmental perception. Next, a Transformer encoder extracts temporal features from the observed motion sequence. A ST-RF attention module captures interaction dynamics, while an adaptive gating mechanism fuses these cues with the encoded features. The Transformer decoder outputs a sequence of control commands, which are integrated through a vehicle kinematics network layers to generate continuous, physically grounded trajectories. Furthermore, we introduce a multi-objective physical constraint loss function enforcing kinematic and dynamic constraints across all predicted agents. Extensive evaluations on the HighD and NGSIM datasets demonstrate that our model achieves significant reductions in RMSE across all prediction horizons, with average decreases of 1.63% and 7.69%, respectively. Additionally, in diverse challenging traffic scenarios, our approach exhibits exceptional robustness, producing trajectories that are both physically plausible and readily interpretable.
