Automotive Collision Avoidance System Field Operational Test Final Program

Automotive Collision Avoidance System Field Operational Test Report: Methodology and Results

S T at Us of Nht S A' S Re Ar-end Crash Prevention Research Program

This paper provides an update on two cooperative research projects being conducted under the National Highway Trafic Safety Administration’s (NHTSA) Rear-End Crash Prevention Program. The first project is the General Motors-Ford Crash Avoidance Metrics Partnership (CAMP) Forward Collision Warning (FCW) work. Since 1995, this project has been aimed at defining and developing pre-competitive enab...

Robust Design and Analysis of Automotive Collision Avoidance Algorithms

Automotive collision avoidance systems help the driver to avoid or mitigate a collision. The main objective of this project is to find a methodology to improve the performance of Volvo’s automotive collision avoidance system by optimizing its configurable parameters. It is important that the parameter setting is chosen in such a way that the automotive collision avoidance system is not too sens...

Error Analysis in a Stereo Vision-Based Pedestrian Detection Sensor for Collision Avoidance Applications

This paper presents an analytical study of the depth estimation error of a stereo vision-based pedestrian detection sensor for automotive applications such as pedestrian collision avoidance and/or mitigation. The sensor comprises two synchronized and calibrated low-cost cameras. Pedestrians are detected by combining a 3D clustering method with Support Vector Machine-based (SVM) classification. ...

Verification of Collision Avoidance Systems Using Reachability Analysis

This paper presents a method for formal verification of automotive collision avoidance systems. Using viability theory and reachability analysis, we define when the system should intervene, i.e. the unsafe set, and when the system should not intervene, i.e. the safe set. Given these sets, we formulate the problem of verifying that a given system does not make incorrect decisions as an optimizat...