Trajectory planning is essential for self-driving vehicles and has stringent requirements for accuracy and efficiency. The existing trajectory planning methods have limitations in the feasibility of planned trajectories and computational efficiency. This paper proposes a life-long learning framework to achieve effective and high-accuracy direct trajectory planning (DTP) tasks. Based on generative adversarial networks (GANs), this study develops a lightweight GDTP model to map the initial/final states and the control action sequence. Additionally, by embedding the GDTP into the rapidly-exploring random tree (RRT), a GDTP-RRT algorithm is further designed for long-distance and multi-stage planning tasks. Taking the tractor-trailer as an application case, we test the proposed method in multiple scenarios with varying characteristics. The experimental results show that the method can plan highly feasible trajectories in a short time, compared with the most applied algorithm – the cubic curve RRT* (CCRRT*). It is found that the tracking errors of our method are 29.1% and 44.1% lower than the CCRRT* in terms of position and heading angle. This paper provides an effective and stable vehicle trajectory planning method for complex self-driving tasks.

Infrastructure-enabled autonomous driving systems have been increasingly applied in confined environments. Automated valet parking (AVP) in smart parking garages is one of the notable applications that require high-precision indoor positioning services. However, the performances of the existing wireless indoor positioning techniques, including Wi-Fi, Bluetooth, and ultra-wideband (UWB), tend to decline substantially as the working time increases and the building environment varies. In this paper, we propose a lifelong framework using crowdsourced data of fully instrumented autonomous vehicles(e.g. vehicles equipping with LiDAR), to maintain the availability and precision of indoor positioning systems. We establish a map-aided deep learning positioning correction model based on continuous data sequences, which utilizes convolution and long short-term memory (LSTM) modules to extract the spatial and temporal features of positioning errors. A local grid map generator is designed and embedded into the correction model to learn the influencing factors of errors from the building environment and facility. A deep-learning-based anomaly detector is designed to keep the lifelong stability of our framework. Based on the proposed method, we develop a lifelong UWB positioning correction system and apply it for the path tracking of AVP in a real underground parking garage. The test results show that the system can maintain positioning correction precision in the environment of varying sensor errors and reduce the positioning error by 60% and the tracking error by 40%. The study showcases an innovative infrastructure-enabled application that can accelerate the widespread use of autonomous driving systems.