RALACs: Action Recognition in Autonomous Vehicles using Interaction Encoding and Optical Flow

TL;DR

This paper introduces RALACs, a novel two-stage action recognition system for autonomous vehicles that leverages interaction encoding and optical flow to improve scene understanding and agent activity detection.

Contribution

It presents a new action recognition framework tailored for road scenes, incorporating attention-based encoding, ROI adaptation, and optical flow fusion, bridging the gap with human action recognition.

Findings

Outperforms baseline on ICCV2021 Road Challenge dataset

Demonstrates effectiveness on real vehicle platform

Enhances situational awareness in autonomous driving

Abstract

When applied to autonomous vehicle (AV) settings, action recognition can enhance an environment model's situational awareness. This is especially prevalent in scenarios where traditional geometric descriptions and heuristics in AVs are insufficient. However, action recognition has traditionally been studied for humans, and its limited adaptability to noisy, un-clipped, un-pampered, raw RGB data has limited its application in other fields. To push for the advancement and adoption of action recognition into AVs, this work proposes a novel two-stage action recognition system, termed RALACs. RALACs formulates the problem of action recognition for road scenes, and bridges the gap between it and the established field of human action recognition. This work shows how attention layers can be useful for encoding the relations across agents, and stresses how such a scheme can be class-agnostic. Furthermore, to address the dynamic nature of agents on the road, RALACs constructs a novel approach to adapting Region of Interest (ROI) Alignment to agent tracks for downstream action classification. Finally, our scheme also considers the problem of active agent detection, and utilizes a novel application of fusing optical flow maps to discern relevant agents in a road scene. We show that our proposed scheme can outperform the baseline on the ICCV2021 Road Challenge dataset and by deploying it on a real vehicle platform, we provide preliminary insight to the usefulness of action recognition in decision making.