Lidar-Based Contour Estimation of Oncoming Vehicles in Pre-Crash Scenarios

The stated goal of the automotive industry is the development of autonomous driving. In addition to comfort, the improvement of vehicle safety is one of the main reasons. One of the new opportunities is that information from the environmental sensors can be used to detect a crash and trigger actuators of passive safety systems before the impact occurs. However, the collision partner as well as the crash parameters must be known in detail to avoid a false activation. While the crash velocity can be precisely determined by radar, LiDAR sensors in forward-looking sensor systems provide accurate information about the geometry of an object. Hence, this paper presents a methodology to estimate the contour of oncoming vehicles in the pre-crash phase using LiDAR. An optimized and fast algorithm derives the main vehicle vertices from the LiDAR point cloud. Afterwards, the relevant vehicle contour and the longitudinal axis is determined. The result is a significantly more detailed vehicle contour compared to conventional bounding boxes. Tests on real cars showed high accuracy and robustness of the estimation on static as well as on dynamic measurements. Next research steps will be included more difficult scenarios and higher velocities.