The recent proliferation of sensing and computing technologies has promoted the rapid development of automated driving. A series of automated driving systems are consecutively released by manufactures in recent years. These systems at different automated driving levels rely on various perception sensors and vehicle computing system to detect the environment and process the real-time data. The configuration of environmental sensing and vehicle computing in the system design stage for facilitating automated driving are attracting more and more attention. In this paper, we investigate the state-of-the-art configuration design schemes of perception system and computing system in automated driving at different levels of automation, involving up to thirty automated driving systems or vehicles. Comprehensive analysis by statistics approaches is followed with the emphasis of underlying design patterns and the cross relationships among environmental sensing, vehicle computing and automated driving levels. Furthermore, from a general perspective, we analyze and conclude the requirement on the perception system and the computing system in automated vehicles, considering typical features in each automated driving level. We expect that this work could serve as a reference for practical engineering on the configuration design for automated driving systems, and promote academic research on relative theories and methodologies for system design.

Traffic signal control is critical for traffic efficiency optimization but is usually constrained by traffic detection methods. The emerging V2I (Vehicle to Infrastructure) technology is capable of providing rich information for traffic detection, thus becoming promising for traffic signal control. Based on parallel simulation, this paper presents a new traffic signal optimization method in a V2I environment. In the proposed method, a predictive optimization problem is formulated, and a cellular automata model is employed as traffic flow model. By using genetic algorithm, the predictive optimization problem is solved online to implement receding horizon control. Simulation results show that the proposed method can improve traffic efficiency in the sense of reducing average delay and number of stops. Meanwhile, simulation also shows that greater communication range brings better performance for reducing the average number of stops. Simulation results show that the proposed V2I-based signal control method can improve traffic efficiency, especially when the traffic volume is relatively high. The proposed algorithm can be applied to traffic signal control to improve traffic efficiency.

A spatial-dependent robust control strategy is proposed for the on-ramp merging problem based on the coordination of the connected and automated vehicles. In the proposed strategy, the planning stage of the merging coordination is weakened while the control stage is strengthened. More specifically, the planning stage mainly forms a virtual platoon containing all vehicles inside the communication zone. In the control stage, the time-varying parameter uncertainties in the model are considered. A spatial-dependent controller with uniform boundedness, uniform ultimate boundedness and robustness is delicately designed for each vehicle in the virtual platoon to analytically calculate the control force in real time. The spatial-dependence means that the safety-related performances of the controller are directly and explicitly bonded with spatial locations such that the collision-avoidance safety is ensured at the most dangerous conflicting merging zone. Since spatial locations in the traffic environment are static, this spatial-dependence endows the proposed strategy more stability and reliability. The effectiveness of the proposed strategy is demonstrated through the verification, ablation and comparison simulation cases.

Mixed periodic and aperiodic tasks with explicit deterministic or probabilistic timing requirements are becoming increasingly deployed in real-time industry control systems. Such systems pose significant challenges to the scheduling algorithms because the failure of scheduling can be catastrophic. In the past decades, significant research effort has been dedicated on the scheduling problems, and various scheduling algorithms were proposed to meet various system requirements and task loads. However, a single fixed scheduling algorithm usually cannot fully satisfy the requirements for a dynamic mixed-task-set, which is commonly found in modern complex real-time control systems. It is thus extremely hard for engineers to design a set of scheduling solutions to guarantee the correctness and optimality under all conditions. Aiming at optimising the scheduling performance in a real-time control system, this paper proposes a Q-learning-based optimisation framework to select proper scheduling algorithms for the mixed-task-set. Built on a three-layer perceptron network, our Q-learning framework is able to efficiently and effectively choose scheduling algorithms that dynamically adapt to the characteristics of task-sets. Experimental results using real-world data proved the effectiveness of the proposed framework.