| dc.description.abstracteng | One of the major challenges to enable automated driving is the perception of other road users in
the host vehicle’s vicinity. Various automotive sensors that provide detailed information about
other traffic participants have been developed to handle this challenge. Of particular interest for
this work are Light Detection and Ranging (LIDAR) and Radio Detection and Ranging (RADAR)
sensors, which generate multiple, spatially distributed, noise corrupted point measurements on
other traffic participants. Based on these point measurements, the traffic participant’s kinematic
and shape parameters have to be estimated.
The choice of a suitable extent model is paramount to accurately track a target’s position, orientation
and other parameters. How well a model performs typically depends on the type of target that
has to be tracked, e.g. pedestrians, bikes or cars, as well as the sensor’s setup and measurement
principle itself. This work considers the creation of extended object models and corresponding
inference strategies for tracking automotive vehicles based on accumulated point cloud data.
We gain insights into the extended object model’s requirements by analysing automotive LIDAR
and RADAR sensor data. This analysis aids in the identification of relevant features from the
measurement’s spatial distribution and their incorporation into an accurate target model. The
analysis lays the foundation for our main contributions.
We developed a constrained Spline-based geometric representation and a corresponding inference
strategy for the contour of cars in LIDAR data.
We further developed a heuristic to account for the integration of the measurement distribution on
cars, generated by LIDAR sensors mounted on the roof of the recording vessel.
Last, we developed an extended target model for cars based on automotive RADAR sensors. The
model provides an interpretation of a learned Gaussian Mixture Model (GMM) as scatter sources
and uses the Probabilistic Multi-Hypothesis Tracker (PMHT) to formulate a closed form Maximum
a Posteriori (MAP) update.
All developed approaches are evaluated on real world data sets. | de |