  Vehicle detection and tracking is a core ingredient for developing autonomous
driving applications in urban scenarios. Recent image-based Deep Learning (DL)
techniques are obtaining breakthrough results in these perceptive tasks.
However, DL research has not yet advanced much towards processing 3D point
clouds from lidar range-finders. These sensors are very common in autonomous
vehicles since, despite not providing as semantically rich information as
images, their performance is more robust under harsh weather conditions than
vision sensors. In this paper we present a full vehicle detection and tracking
system that works with 3D lidar information only. Our detection step uses a
Convolutional Neural Network (CNN) that receives as input a featured
representation of the 3D information provided by a Velodyne HDL-64 sensor and
returns a per-point classification of whether it belongs to a vehicle or not.
The classified point cloud is then geometrically processed to generate
observations for a multi-object tracking system implemented via a number of
Multi-Hypothesis Extended Kalman Filters (MH-EKF) that estimate the position
and velocity of the surrounding vehicles. The system is thoroughly evaluated on
the KITTI tracking dataset, and we show the performance boost provided by our
CNN-based vehicle detector over a standard geometric approach. Our lidar-based
approach uses about a 4% of the data needed for an image-based detector with
similarly competitive results.
