VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users

TL;DR

This paper introduces VRUNet, a multi-task learning model that predicts the intentions and future paths of vulnerable road users like pedestrians and cyclists using video data, enhancing autonomous vehicle safety.

Contribution

The paper presents a novel multi-task learning approach that jointly predicts actions, crossing intent, and trajectories of vulnerable road users from video sequences.

Findings

Achieves state-of-the-art performance on JAAD dataset.

Benefits from joint learning of actions and trajectories.

Utilizes 2D human pose features and scene context effectively.

Abstract

Advanced perception and path planning are at the core for any self-driving vehicle. Autonomous vehicles need to understand the scene and intentions of other road users for safe motion planning. For urban use cases it is very important to perceive and predict the intentions of pedestrians, cyclists, scooters, etc., classified as vulnerable road users (VRU). Intent is a combination of pedestrian activities and long term trajectories defining their future motion. In this paper we propose a multi-task learning model to predict pedestrian actions, crossing intent and forecast their future path from video sequences. We have trained the model on naturalistic driving open-source JAAD dataset, which is rich in behavioral annotations and real world scenarios. Experimental results show state-of-the-art performance on JAAD dataset and how we can benefit from jointly learning and predicting actions and trajectories using 2D human pose features and scene context.