Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning

TL;DR

This paper introduces an invariant environment representation for automated driving decision making using deep reinforcement learning, enabling better generalization across diverse scenarios and occlusion conditions.

Contribution

It proposes a novel environment representation that improves generalization in automated driving decision making and demonstrates its effectiveness through extensive testing.

Findings

Agents successfully generalize to unseen scenarios

Invariant representation improves decision-making robustness

Occlusion handling maintains performance at intersections

Abstract

Data driven approaches for decision making applied to automated driving require appropriate generalization strategies, to ensure applicability to the world's variability. Current approaches either do not generalize well beyond the training data or are not capable to consider a variable number of traffic participants. Therefore we propose an invariant environment representation from the perspective of the ego vehicle. The representation encodes all necessary information for safe decision making. To assess the generalization capabilities of the novel environment representation, we train our agents on a small subset of scenarios and evaluate on the entire diverse set of scenarios. Here we show that the agents are capable to generalize successfully to unseen scenarios, due to the abstraction. In addition we present a simple occlusion model that enables our agents to navigate intersections with occlusions without a significant change in performance.