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Trajectory planning plays a crucial role in autonomous driving. It generates feasible geometric paths within a certain time horizon to guide autonomous vehicles. However, typical planning methods give little consideration to the motion uncertainty of traffic objects. Therefore, this article proposes a predictive trajectory planning method based on a spatiotemporal risk potential field. And the key notion of this method is to separate the coupled executions, i.e., trajectory generation and risk assessment, into two parallel and independent modules in the curvilinear coordinate. First, the current spatial risks are quantified by artificial potential-like functions. Then, we measure the probabilities of surrounding vehicles driving toward each lane using an interacting multiple model structure. The temporal evolution of the spatial field is computed by incorporating both the probabilities and predicted trajectories. Meanwhile, the generation module focuses on providing sufficient trajectory candidates for the ego vehicle. To allocate computing resources reasonably, feasibility probabilities are calculated with a relational grid, and then candidates are biased sampled in lateral and longitudinal directions. Finally, taking into account driving safety, comfort, and efficiency, the planner selects an optimal trajectory by minimizing a well-designed cost function. In this way, the feasibility of planned trajectories can be greatly improved with limited time consumption. The performance of this proposed method is evaluated in various scenarios using a high-fidelity Unity3D environment. [ABSTRACT FROM AUTHOR]

Signalized intersections play a vital role in addressing the issue of transportation efficiency and vehicle fuel economy in urban areas. Meanwhile, with the development of Connected and Automated Vehicles (CAVs), the mixed traffic environment composed of traffic participants with differing intelligent levels will become an important stage of the intelligent transportation system. Considering the changes in the mixed traffic environment, this paper proposes a Coupled Vehicle-Signal Control (CVSC) method to optimize the traffic signal timing and driving trajectories of CAVs at the same time, with the goals of traffic efficiency improvement and energy saving respectively. The signal timing is continuously optimized to minimize the total delay at the intersection. CAVs generate eco-driving trajectories using the received signal timing information and the planned arrival time to reduce fuel consumption. Finally, simulation experiments were been carried out to verify the control effect of the proposed CVSC. On the one hand, the influence of the involved parameters on the optimization results was analyzed and discussed. On the other hand, the proposed CVSC method was compared with the traditional CACC control and the classic eco-driving model, GlidePath. The research results show that the proposed CVSC method can effectively improve the performance of signalized intersections. When the penetration rate of CAVs is greater than 40%, this method can save fuel consumption by 6%-14% and increase the average speed by 1%-5%. [ABSTRACT FROM AUTHOR]