End-to-End Learning of Behavioural Inputs for Autonomous Driving in Dense Traffic

Abstract

Trajectory sampling in the Frenet(road-aligned) frame, is one of the most popular methods for motion planning of autonomous vehicles. It operates by sampling a set of behavioral inputs, such as lane offset and forward speed, before solving a trajectory optimization problem conditioned on the sampled inputs. The sampling is handcrafted based on simple heuristics, does not adapt to driving scenarios, and is oblivious to the capabilities of downstream trajectory planners. In this paper, we propose an end-to-end learning of behavioral input distribution from expert demonstrations or in a self-supervised manner. We embed a novel differentiable trajectory optimizer as a layer in neural networks, allowing us to update behavioral inputs by considering the optimizer's feedback. Moreover, our end-to-end approach also ensures that the learned behavioral inputs aid the convergence of the optimizer. We improve the state-of-the-art in the following aspects. First, we show that learned behavioral inputs substantially decrease collision rate while improving driving efficiency over handcrafted approaches. Second, our approach outperforms model predictive control methods based on sampling-based optimization.