Conditional Vehicle Trajectories Prediction in CARLA Urban Environment

Imitation learning is becoming more and more successful for autonomous driving. End-to-end (raw signal to command) performs well on relatively simple tasks (lane keeping and navigation). Mid-to-mid (environment abstraction to mid-level trajectory representation) or direct perception (raw signal to performance) approaches strive to handle more complex, real life environment and tasks (e.g. In this work, we show that complex urban situations can be handled with raw signal input and mid-level representation. W e build a hybrid end-to-mid approach predicting trajectories for neighbor vehicles and for the ego vehicle with a conditional navigation goal. W e propose an original architecture inspired from social pooling LSTM taking low and mid level data as input and producing trajectories as polynomials of time. W e introduce a label augmentation mechanism to get the level of generalization that is required to control a vehicle. The performance is evaluated on CARLA 0.8 benchmark, showing significant improvements over previously published state of the art. 1. Introduction Modular pipelines [32] are the most used approach to autonomous driving. The advantage is that the modules are interpretable and relatively mature, in particular on the perception side with the success of deep learning for object detection ([13, 20] among many others). However, the complexity of the interactions in the real world causes the pipeline to be also complex, especially in the planning and decision modules.