This paper explores the use of stixels in a probabilistic stereo vision-based collision-warning system that can be part of an ADAS for intelligent vehicles. In most current systems, collision warnings are based on radar or on monocular vision using pat- tern recognition (and ultra-sound
for park assist). Since detect- ing collisions is such a core functionality of intelligent vehicles, redundancy is key. Therefore, we explore the use of stereo vi- sion for reliable collision prediction. Our algorithm consists of a Bayesian histogram filter that provides the probability of
collision for multiple interception regions and angles towards the vehicle. This could additionally be fused with other sources of informa- tion in larger systems. Our algorithm builds upon the dispar- ity Stixel World that has been developed for efficient automotive vision applications. Combined
with image flow and uncertainty modeling, our system samples and propagates asteroids, which are dynamic particles that can be utilized for collision prediction. At best, our independent system detects all 31 simulated collisions (2 false warnings), while this setting generates 12 false warnings
on the real-world data.
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Document Type: Research Article
January 13, 2019
This article was made available online on January 13, 2019 as a Fast Track article with title: "From stixels to asteroids: Towards a collision warning system using stereo vision".
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