Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning

TL;DR

This paper introduces a neural network architecture with attention mechanisms for path integral inverse reinforcement learning, enabling extended horizon reward prediction and improved interaction modeling in automated driving scenarios.

Contribution

It proposes a novel neural network with policy and temporal attention mechanisms for better sequential reward prediction in dynamic driving environments.

Findings

Policy attention focuses on collision-free trajectories.

Temporal attention captures persistent interactions over time.

Model performs well in complex simulated driving situations.

Abstract

General-purpose trajectory planning algorithms for automated driving utilize complex reward functions to perform a combined optimization of strategic, behavioral, and kinematic features. The specification and tuning of a single reward function is a tedious task and does not generalize over a large set of traffic situations. Deep learning approaches based on path integral inverse reinforcement learning have been successfully applied to predict local situation-dependent reward functions using features of a set of sampled driving policies. Sample-based trajectory planning algorithms are able to approximate a spatio-temporal subspace of feasible driving policies that can be used to encode the context of a situation. However, the interaction with dynamic objects requires an extended planning horizon, which depends on sequential context modeling. In this work, we are concerned with the sequential reward prediction over an extended time horizon. We present a neural network architecture that uses a policy attention mechanism to generate a low-dimensional context vector by concentrating on trajectories with a human-like driving style. Apart from this, we propose a temporal attention mechanism to identify context switches and allow for stable adaptation of rewards. We evaluate our results on complex simulated driving situations, including other moving vehicles. Our evaluation shows that our policy attention mechanism learns to focus on collision-free policies in the configuration space. Furthermore, the temporal attention mechanism learns persistent interaction with other vehicles over an extended planning horizon.