Periodic Event-triggered Fault Detection for Safe Platooning Control of Intelligent and Connected Vehicles

Fault detection is not only a useful approach to guarantee the safety of a vehicle platooning system but also an indispensable part of functional safety for future connected automated vehicle development. This paper mainly concentrates on the network-based fault detection problem of a vehicle platoon with undirected topologies under a periodic event-triggered strategy (PETS). Firstly, we present a periodic event-triggered fault detection filter to generate a residual signal for this vehicle platoon subject to actuator faults and external disturbances, where PETS is employed to reduce bandwidth utilization and save communication resources. Secondly, by using the network-based fault detection filter, the vehicle platooning system, and a fault weighting system, a residual system is developed to formulate the design of the fault detection filter problem as an H∞ problem. Thirdly, based on Lyapunov-Krasovskii functionals, sufficient conditions are established to ensure that the residual system fulfills asymptotic stability with H∞ performance, together with a threshold is also designed for each vehicle to judge whether the fault happens or not. Finally, numerical examples and field experiments are conducted to verify our findings

Design and Occupant-Protection Performance Analysis of a New Tubular Driver Airbag

An airbag is an effective protective device for vehicle occupant safety, but may cause unexpected injury from the excessive energy of ignition when it is deployed. This paper focuses on the design of a new tubular driver airbag from the perspective of reducing the dosage of gas generant. Three different dummies were selected for computer simulation to investigate the stiffness and protection performance of the new airbag. Next, a multi-objective optimization of the 50th percentile dummy was conducted. The results show that the static volume of the new airbag is only about 1/3 of the volume of an ordinary one, and the injury value of each type of dummy can meet legal requirements while reducing the gas dosage by at least 30%. The combined injury index (Pcomb) decreases by 22% and the gas dosage is reduced by 32% after optimization. This study demonstrates that the new tubular driver airbag has great potential for protection in terms of reducing the gas dosage. Keywords: New tubular airbag, Occupant protection, Multi-objective optimizatio

V2I-based startup assistance system at signalized intersections

Traffic delays are caused by unskilled vehicle operation and driver distraction during the startup process at signalized intersections. To address this issue, we propose a V2I-based driver assistance system that can acquire the current traffic signal status and provide drivers with startup assistance. This article presents the proposed system’s architecture and an assistance algorithm, which contains two types of driver assistance methods: startup prompting and automatic startup control. The automatic startup control method, based on fuzzy logic control, is validated in simulation tests. We also implement startup prompting using a prototype system and validate its performance in field tests. The test results suggest that the proposed assistance algorithm can help drivers start up their vehicles with less delay, which will significantly improve traffic efficiency

Data-Enabled Tire-Road Friction Estimation Based on Explainable Dynamics Mechanism under Straight Stationary Driving Maneuvers

The tire-road friction coefficient (TRFC) is the critical parameter that significantly improves the control performance of distributed electric vehicles. Nonetheless, achieving precise TRFC estimation during straight stationary driving maneuvers, characterized by constant longitudinal speed (e.g., where the longitudinal acceleration is nearly zero) on a straight road, poses a particularly formidable challenge. In the paper, we propose a new learning strategy that leverages multi-domain fusion feature extraction in both the time domain and time-frequency domain to estimate the TRFC during straight stationary driving maneuvers. Specifically, the frequency response function of the in-wheel-motor-drive system first is inferred from the longitudinal dynamics model and single wheel dynamics model. Then, the input selection of learning strategy is determined through frequency response characteristics analysis and explainable dynamics mechanism. In addition, a parallel spatial-temporal convolutional neural network (PSTCNN) is built to extract features in both the time domain and in the time-frequency domain, respectively. Finally, the TRFC learning strategy is verified by experimental tests on different road surfaces. Our results demonstrate that the proposed methodology is capable of estimating the TRFC with a lower error than the traditional learning-based method and the classical slip-slope method.</p

Model predictive control–based cooperative lane change strategy for improving traffic flow

Lane change has attracted more and more attention in recent years for its negative impact on traffic safety and efficiency. However, few researches addressed the multi-vehicle cooperation during lane change process. In this article, feasibility criteria of lane change are designed, which considers the acceptable acceleration/deceleration of neighboring vehicles; meanwhile, a cooperative lane change strategy based on model predictive control is proposed in order to attenuate the adverse impacts of lane change on traffic flow. The proposed strategy implements the centralized decision making and active cooperation among the subject vehicle performing lane change in the subject lane and the preceding vehicle and the following vehicle in the target lane during lane change. Using model predictive control, safety, comfort, and traffic efficiency are integrated as the objectives, and lane change process is optimized. Numerical simulation results of the cooperative lane change strategy suggest that the deceleration of following vehicle can be weakened and further the shock wave propagated in traffic flow can be alleviated to some degree compared with traditional lane change

Space Discretization-Based Optimal Trajectory Planning for Automated Vehicles in Narrow Corridor Scenes

The narrow corridor is a common working scene for automated vehicles, where it is pretty challenging to plan a safe, feasible, and smooth trajectory due to the narrow passable area constraints. This paper presents a space discretization-based optimal trajectory planning method for automated vehicles in a narrow corridor scene with the consideration of travel time minimization and boundary collision avoidance. In this method, we first design a mathematically-described driving corridor model. Then, we build a space discretization-based trajectory optimization model in which the objective function is travel efficiency, and the vehicle-kinematics constraints, collision avoidance constraints, and several other constraints are proposed to ensure the feasibility and comfortability of the planned trajectory. Finally, the proposed method is verified with both simulations and field tests. The experimental results demonstrate the trajectory planned by the proposed method is smoother and more computationally efficient compared with the baseline methods while significantly reducing the tracking error indicating the proposed method has huge application potential in trajectory planning in the narrow corridor scenario for automated vehicles

Space Discretization-Based Optimal Trajectory Planning for Automated Vehicles in Narrow Corridor Scenes

The narrow corridor is a common working scene for automated vehicles, where it is pretty challenging to plan a safe, feasible, and smooth trajectory due to the narrow passable area constraints. This paper presents a space discretization-based optimal trajectory planning method for automated vehicles in a narrow corridor scene with the consideration of travel time minimization and boundary collision avoidance. In this method, we first design a mathematically-described driving corridor model. Then, we build a space discretization-based trajectory optimization model in which the objective function is travel efficiency, and the vehicle-kinematics constraints, collision avoidance constraints, and several other constraints are proposed to ensure the feasibility and comfortability of the planned trajectory. Finally, the proposed method is verified with both simulations and field tests. The experimental results demonstrate the trajectory planned by the proposed method is smoother and more computationally efficient compared with the baseline methods while significantly reducing the tracking error indicating the proposed method has huge application potential in trajectory planning in the narrow corridor scenario for automated vehicles