A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios

TL;DR

This paper introduces a biologically inspired neural network model for robust and adaptive visual collision detection in complex driving environments, enhancing autonomous vehicle safety.

Contribution

A novel inhibition mechanism inspired by locust visual pathways that adapts to background complexity for improved collision perception.

Findings

Effective in complex dynamic scenes

Suitable for real-time collision detection

Demonstrated on vehicle crash videos

Abstract

This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios. This research takes inspiration from a large-field looming sensitive neuron, i.e., the lobula giant movement detector (LGMD) in the locust's visual pathways, which represents high spike frequency to rapid approaching objects. Building upon our previous models, in this paper we propose a novel inhibition mechanism that is capable of adapting to different levels of background complexity. This adaptive mechanism works effectively to mediate the local inhibition strength and tune the temporal latency of local excitation reaching the LGMD neuron. As a result, the proposed model is effective to extract colliding cues from complex dynamic visual scenes. We tested the proposed method using a range of stimuli including simulated movements in grating backgrounds and shifting of a natural panoramic scene, as well as vehicle crash video sequences. The experimental results demonstrate the proposed method is feasible for fast collision perception in real-world situations with potential applications in future autonomous vehicles.