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

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.

Forecasting Time-to-Collision from Monocular Video: Feasibility, Dataset, and Challenges

“…Automated collision avoidance technology is an indispensable part of mobile robots. As an alternative to traditional approaches using multi-modal sensors, purely image-based collision avoidance strategies [1], [2], [3], [4] have recently gained attention in robotics. These image-based approaches use the power of large data to detect immediate collision as a binary variable -collision or no collision.…”