Self-driving cars require a holistic perception of
their environment. To achieve this requirement, a plethora of
sensor technologies exists e.g. RGB-camera, ultra-sonic and
radar. Those sensor technologies have different range, as well as
resolution and behave differently with varying weather conditions.
Another technology is Light Detection and Ranging (LiDAR),
which enables precise distance measurements. In combination
with RGB-cameras, ultra-sonic, and radar, LiDAR closes the gap
to enable the holistic perception of the environment.
Due to limited experience with LiDAR sensors, there is a lack
of understanding how to detect, track, and classify objects (e.g.
cars, guardrails) using LiDAR data. In this paper, we propose an
architecture to detect, track, and classify objects based on LiDAR
measurements in highway scenarios.We evaluate our architecture
using preliminary sensor data obtained from a setup including
six Ibeo Lux sensors and additional a roof mounted Velodyne
HDL-64E
