A Graph Representation for Autonomous Driving

For human drivers, an important aspect of learning to drive is knowing how to pay attention to areas of the roadway that are critical for decision-making while simultaneously ignoring distractions. Similarly, the choice of roadway representation is critical for good performance of an autonomous driving system. An effective representation should be compact and permutation-invariant, while still representing complex vehicle interactions that govern driving decisions. This paper introduces the Graph Representation for Autonomous Driving (GRAD); GRAD generates a global scene representation using a space-time graph which incorporates the estimated future trajectories of other vehicles. We demonstrate that GRAD outperforms the best performing social attention representation on a simulated highway driving task in high traffic densities and also has a low computational complexity in both single and multi-agent settings.