Online Obstacle Trajectory Prediction for Autonomous Buses

We tackle the problem of achieving real-world autonomous driving for buses, where the task is to perceive nearby obstacles and predict their motion in the time ahead, given current and past information of the object. In this paper, we present the development of a modular pipeline for the long-term prediction of dynamic obstacles' trajectories for an autonomous bus. The pipeline consists of three main tasks, which are the obstacle detection task, tracking task, and trajectory prediction task. Unlike most of the existing literature that performs experiments in the laboratory, our pipeline's modules are dependent on the introductory modules in the pipeline-it uses the output of previous modules. This best emulates real-world autonomous driving and reflects the errors that may accumulate and cascade from previous modules with less than 100% accuracy. For the trajectory prediction task, we propose a training method to improve the module's performance and attain a run-time of 10 Hz. We present the practical problems that arise from realising ready-to-deploy autonomous buses and propose methods to overcome these problems for each task. Our Singapore autonomous bus (SGAB) dataset evaluated the pipeline's performance. The dataset is publicly available online.