Abstract

International audience; Understanding driver behaviors is an important need for the Advanced Driver Assistance Systems. In particular, the pedestrian detection systems become extremely distracting and annoying when they inform the driver with unnecessary warning messages. In this paper, we propose to study the driver behaviors whenever a pedestrian appears in front of the vehicle. A method based on the driving actions and the Hidden Markov Model (HMM) algorithm is developed to classify the driver awareness of pedestrian and the driver unawareness of pedestrian. The method is successfully validated using the collected data from the experiments that are conducted on a driving simulator. Furthermore, two simple methods based on the static parameters such as the Time-To-Collision and the Required Deceleration Parameter are also applied to our problem and are compared to the proposed method. The result shows a significant improvement of the HMM-based method compared to the simple ones.

Abstract

International audience; Advanced Driver Assistance Systems (ADAS) are designed to help drivers improve driving safety. However, automation modifying the way drivers interact with their vehicle, it is important to avoid negative safety impacts. In particular, the change in drivers’ behavior introduced by ADAS in situations they are not designed for, should be carefully examined. We carried out an experiment on a driving simulator to study drivers’ reaction in an obstacle avoidance situation, when using a lateral control assistance system. A detailed analysis of the avoidance maneuver is presented. Results show that assisted and non-assisted drivers equally succeeded in avoiding the obstacle. However, further analyses tend to show an influence of the assistance system on drivers’ first reaction.

Abstract

In this paper, we present an optimized 3D stereoscopic display based on parallax barriers for a driving simulator. The overall purpose of the simulator is to enable user studies in a reproducible environment under controlled conditions to test and evaluate advanced driver assistance systems. Our contribution and the focus of this article is a visualization based on parallax barriers with (I) a-priori optimized barrier patterns and (II) an iterative calibration algorithm to further reduce visualization errors introduced by production inaccuracies. The result is an optimized 3D stereoscopic display perfectly integrated into its environment such that a single user in the simulator environment sees a stereoscopic image without having to wear specialized eye-wear.