Collision Avoidance by Utilizing Dynamic Road Friction Information
Paper i proceeding, 2020

Advanced driver assistance systems require an autonomous emergency braking (AEB) system that considers varying tire-to-road friction conditions to estimate minimum emergency braking distance for collision avoidance. Conventional AEB systems, either do not consider the friction at all or assume constant friction, even though the actual road friction profile is dynamic. Ignoring the dynamic friction conditions leads to inefficient operation under slippery road conditions. Therefore, in this work, we present a novel AEB system that exploits friction information ahead of the ego-vehicle that is predicted using on-board vehicle sensors as well with cloud services. The proposed algorithm utilizes the predicted dynamic friction profile and calculates the last braking point to prevent a collision. We implement a simplified vehicle model that enables fast online computations to evaluate the braking distance. Next, the braking sequence is initiated by an anti-lock braking system with slip control. The performance of the proposed algorithm and controller are simulated and evaluated in terms of the braking distance metric that evaluates the relative distance between the ego-vehicle and the threat vehicle at a standstill. The proposed system achieves an improvement of up to 95.8% for varying friction profiles when compared to braking error obtained by constant friction profiles. Furthermore, the proposed system is capable of preventing collisions in all experimental test, thus making it suitable for designing active safety functionalities.

autonomous emergency braking

collision avoidance

Advanced driver assistance systems

dynamic friction profile


Jonas Herzfeld

Student vid Chalmers

Sanjiv Thottathodhi

Student vid Chalmers

Mats Jonasson

Student vid Chalmers

Srikar Muppirisetty

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Sohini Roychowdhury

Jonas Sjöberg

Chalmers, Elektroteknik, System- och reglerteknik

Conference Record - Asilomar Conference on Signals, Systems and Computers

10586393 (ISSN)

Vol. 2020-November 1425-1429 9443294
9780738131269 (ISBN)

54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Pacific Grove, USA,


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