ASTEROIDS: A Stixel Tracking Extrapolation-based Relevant Obstacle Impact Detection System

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ASTEROIDS: A Stixel Tracking Extrapolation-based Relevant Obstacle Impact Detection System

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Sanberg, Willem P. / Dubbelman, Gijs / de With, Peter H.N.

This paper presents a vision-based collision-warning system for ADAS in intelligent vehicles, with a focus on urban scenarios. In most current systems, collision warnings are based on radar, or on monocular vision using pattern recognition. Since detecting collisions is a core functionality of intelligent vehicles, redundancy is essential, so that we explore the use of stereo vision. First, our approach is generic and class-agnostic, since it can detect general obstacles that are on a colliding path with the ego-vehicle without relying on semantic information. The framework estimates disparity and flow from a stereo video stream and calculates stixels. Then, the second contribution is the use of the new asteroids concept as a consecutive step. This step samples particles based on a probabilistic uncertainty analysis of the measurement process to model potential collisions. Third, this is all enclosed in a Bayesian histogram filter around a newly introduced time-to-collision versus angle-of-impact state space. The evaluation shows that the system correctly avoids any false warnings on the real-world KITTI dataset, detects all collisions in a newly simulated dataset when the obstacle is higher than 0.4m, and performs excellent on our new qualitative real-world data with near-collisions, both in daytime and nighttime conditions.

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Dokumentinformationen

Titel :

ASTEROIDS: A Stixel Tracking Extrapolation-based Relevant Obstacle Impact Detection System

Beteiligte:

Sanberg, Willem P. (Autor:in) / Dubbelman, Gijs (Autor:in) / de With, Peter H.N. (Autor:in)

Erscheinungsdatum :

2021-03-01

Anmerkungen:

Sanberg , W P , Dubbelman , G & de With , P H N 2021 , ' ASTEROIDS: A Stixel Tracking Extrapolation-based Relevant Obstacle Impact Detection System ' , IEEE Transactions on Intelligent Vehicles , vol. 6 , no. 1 , 9085910 , pp. 34-46 . https://doi.org/10.1109/TIV.2020.2992086

DOI :

https://doi.org/10.1109/TIV.2020.2992086

Medientyp :

Aufsatz (Zeitschrift)

Format :

Elektronische Ressource

Sprache :

Englisch

Schlagwörter :

ADAS , Bayesian Histogram Filter , Collision Warning , Stereo vision , Time to Collision

Klassifikation :

DDC: