Connected Autonomous Vehicle Motion Planning with Video Predictions from Smart, Self-Supervised Infrastructure

TL;DR

This paper presents a novel approach where smart infrastructure generates self-supervised video predictions of road users, which are then used to improve autonomous vehicle motion planning in complex urban environments.

Contribution

It introduces a modified SSTA framework that predicts future occupancy instead of raw video, reducing data size and enhancing CAV planning capabilities.

Findings

Predictions effectively aid CAV motion planning in crowded urban scenarios.

Modified SSTA reduces data footprint of predictions.

Numerical experiments validate the approach's practicality.

Abstract

Connected autonomous vehicles (CAVs) promise to enhance safety, efficiency, and sustainability in urban transportation. However, this is contingent upon a CAV correctly predicting the motion of surrounding agents and planning its own motion safely. Doing so is challenging in complex urban environments due to frequent occlusions and interactions among many agents. One solution is to leverage smart infrastructure to augment a CAV's situational awareness; the present work leverages a recently proposed "Self-Supervised Traffic Advisor" (SSTA) framework of smart sensors that teach themselves to generate and broadcast useful video predictions of road users. In this work, SSTA predictions are modified to predict future occupancy instead of raw video, which reduces the data footprint of broadcast predictions. The resulting predictions are used within a planning framework, demonstrating that this design can effectively aid CAV motion planning. A variety of numerical experiments study the key factors that make SSTA outputs useful for practical CAV planning in crowded urban environments.