Conception of a High-Level Perception and Localization System for Autonomous Driving.

This paper describes the conception of a high level, compact, scalable, and long autonomy perception and localization system for autonomous driving applications. Our benchmark is composed of a high resolution lidar (128 channels), a stereo global shutter camera, an inertial navigation system, a time server, and an embedded computer. In addition, in order to acquire data and build multi-modal datasets, this system embeds two perception algorithms (RBNN detection, DCNN detection) and one localization algorithm (lidar-based localization) to provide real-time advanced information such as object detection and localization in challenging environments (lack of GPS).

Risk assessment in ramps for heavy vehicles--A French study.

This paper presents the results of a study dealing with the risk for heavy vehicles in ramps. Two approaches are used. On one hand, statistics are applied on several accidents databases to detect if ramps are more risky for heavy vehicles and to define a critical value for longitudinal slope.