Feature-level fusion of laser scanner and video data for advanced driver assistance systems

Abstract

Advanced driver assistance systems aim at an improved traffic safety, enhanced comfort and driving pleasure. Sensors perceive the objects surrounding the vehicle and produce an environment description. The assistance systems support the driver by assessing the situation recognized by this vehicle environment description. Current research in the area of advanced driver assistance systems aims at increased functionality. Comfort systems, such as the ACC, are expected to support the driver not only in normal driving phases, but also in more complex situations such as traffic jams. Safety systems will trigger collision avoidance or mitigation measures in a number of potential crash configurations and not only in well defined rear end collision situations. These future advanced applications impose strong requirements on the sensors and demand novel and complex signal processing algorithms for vehicle detection, tracking and situation assessment. The present work addresses challenging traffic scenarios. Novel sensor signal processing, sensor data fusion and tracking algorithms were developed which provide precise and consistent motion estimates for complex intersection scenarios, lane change maneuvers of vehicles, trucks on neighbouring lanes and highly dynamic situations. A novel emergency brake application offers a general solution to the situation assessment and aims in particular at so far unresolved intersection scenarios.