Individual mobility on roads has a noticeable impact upon peoples' lives, including
traffic accidents resulting in severe, or even lethal injuries. Therefore the main goal when
operating a vehicle is to safely participate in road-traffic while minimising the adverse
effects on our environment. This goal is pursued by road safety measures ranging from
safety-oriented road design to driver assistance systems. The latter require exteroceptive
sensors to acquire information about the vehicle's current environment.
In this thesis an efficient resource allocation for automotive vision systems is proposed.
The notion of allocating resources implies the presence of processes that observe the whole
environment and that are able to effeciently direct attentive processes. Directing attention
constitutes a decision making process dependent upon the environment it operates in, the
goal it pursues, and the sensor resources and computational resources it allocates. The
sensor resources considered in this thesis are a subset of the multi-modal sensor system on
a test vehicle provided by Audi AG, which is also used to evaluate our proposed resource
allocation system.
This thesis presents an original contribution in three respects. First, a system architecture
designed to efficiently allocate both high-resolution sensor resources and computational
expensive processes based upon low-resolution sensor data is proposed. Second,
a novel method to estimate 3-D range motion, e cient scan-patterns for spin image based
classifiers, and an evaluation of track-to-track fusion algorithms present contributions in
the field of data processing methods. Third, a Pareto efficient multi-objective resource
allocation method is formalised, implemented, and evaluated using road traffic test sequences
