Probabilistic Grid-based Collision Risk Prediction for Driving Application

International audienceIn the recent years, more and more modern cars have been equipped with perception capabilities. One of the key applications of such perception systems is the estimation of a risk of collision. This is necessary for both Advanced Driver Assistance Systems and Autonomous Navigation. Most approach for risk estimation propose to detect and track the dynamic objects in the scene. Then the risk is estimated as a Time To Collision (TTC) by projecting the object's trajectory in the future. In this paper, we propose a new grid-based approach for collision risk prediction, based on the Hybrid-Sampling Bayesian Occupancy Filter framework. The idea is to compute an estimation of the TTC for each cell of the grid, instead of reasoning on objects. This strategy avoids to solve the difficult problem of multi-objects detection and tracking and provides a probabilistic estimation of the risk associated to each TTC value. After promising initial results, we propose in this paper to evaluate the relevance of the method for real on-road applications, by using a real-time implementation of our method in an experimental vehicle

Robust Obstacle Detection based on Dense Disparity Maps

Obstacle detection is an important component for many autonomous vehicle navigation systems. Several methods for obstacle detection have been proposed using various active sensors such as radar, sonar and laser range finders. Vision based techniques have the advantage of low cost and provide a large amount of information about the environment around an intelligent vehicle. This paper deals with the development of an accurate and efficient vision based obstacle detection method which relies on a wavelet analysis. The development system will be integrated on the Cybercar platform which is a road vehicle with fully automated driving capabilities

Experimental assessment of the RESCUE collision mitigation system

Road-traffic-incident analysis has shown that 52% of incidents are caused by a collision between two vehicles or between a vehicle and an obstacle. In this paper, the REduce Speed of Collision Under Emergency (RESCUE) collision-mitigation system (version 1.0) is presented and evaluated toward various typical road situations. The aim of the RESCUE system is to decrease the kinetic energy dissipated during a collision through automatic emergency braking that occurs 1 s before the collision. This emergency braking is triggered by an alarm coming from a decision unit taking into consideration the results of a generic obstacle-detection system-based on fusion between stereovision and laser scanner-and a warning area in front of the vehicle. The different subsystems are presented. Then, the behavior of the RESCUE collision-mitigation system toward various typical dangerous road situations is assessed through systematic tests. These quantitative tests are completed by qualitative ones carried out on 737 km of open roads (freeways, highways, rural roads, downtown) to provide a more precise idea about the false-alarm rate. The experiments show the system is promising in terms of reliability, genericity, and efficienc