[Show abstract][Hide abstract] ABSTRACT: Several advanced driver assistance systems realizing elementary perception and analysis tasks have been introduced to market in recent years. For example, collision mitigation brake systems detect the distance and relative velocity of vehicles in front to assess the risk of a rear-end collision in a clearly defined following situation. In order to go beyond such elementary analysis tasks, todaypsilas research is focusing more and more on powerful perception systems for driver assistance. We believe computer vision will play a central role for achieving a full understanding of generic traffic situations. Besides individual processing algorithms, general vision architectures enabling integrated and more flexible processing are needed. Here we present the first instantiation of a vision architecture for driver assistance systems inspired by the human visual system that is based on task-dependent perception. Core element of our system is a state of the art attention system integrating bottom-up and top-down visual saliency. Combining this task-dependent tunable visual saliency with object recognition and tracking enables for instance warnings according to the context of the scene. We demonstrate the performance of our approach in a construction site setup, where a traffic jam ending within the site is a dangerous situation that the system has to identify in order to warn the driver.
[Show abstract][Hide abstract] ABSTRACT: Research on computer vision systems for driver assistance resulted in a variety of isolated approaches mainly performing very specialized tasks like, e. g., lane keeping or traffic sign detection. However, for a full understanding of generic traffic situations, integrated and flexible approaches are needed. We here present a highly integrated vision architecture for an advanced driver assistance system inspired by human cognitive principles. The system uses an attention system as the flexible and generic front-end for all visual processing, allowing a task-specific scene decomposition and search for known objects (based on a short term memory) as well as generic object classes (based on a long term memory). Knowledge fusion, e. g., between an internal 3D representation and a reliable road detection module improves the system performance. The system heavily relies on top-down links to modulate lower processing levels, resulting in a high system robustness.
at - Automatisierungstechnik 01/2008; 56(11-11):575-584. DOI:10.1524/auto.2008.0737 · 0.19 Impact Factor