The Importance of Prior Knowledge in Precise Multimodal Prediction

TL;DR

This paper introduces a novel method that incorporates structured priors into probabilistic motion forecasting for self-driving cars, improving safety and accuracy by leveraging reinforcement learning to handle complex road scenarios.

Contribution

It proposes a framework using REINFORCE to include non-differentiable priors in probabilistic motion prediction, enhancing safety and precision in autonomous driving.

Findings

Improved motion forecast accuracy on real-world datasets

Enhanced safety of motion plans in complex scenarios

Better map understanding through prior integration

Abstract

Roads have well defined geometries, topologies, and traffic rules. While this has been widely exploited in motion planning methods to produce maneuvers that obey the law, little work has been devoted to utilize these priors in perception and motion forecasting methods. In this paper we propose to incorporate these structured priors as a loss function. In contrast to imposing hard constraints, this approach allows the model to handle non-compliant maneuvers when those happen in the real world. Safe motion planning is the end goal, and thus a probabilistic characterization of the possible future developments of the scene is key to choose the plan with the lowest expected cost. Towards this goal, we design a framework that leverages REINFORCE to incorporate non-differentiable priors over sample trajectories from a probabilistic model, thus optimizing the whole distribution. We demonstrate the effectiveness of our approach on real-world self-driving datasets containing complex road topologies and multi-agent interactions. Our motion forecasts not only exhibit better precision and map understanding, but most importantly result in safer motion plans taken by our self-driving vehicle. We emphasize that despite the importance of this evaluation, it has been often overlooked by previous perception and motion forecasting works.