Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance

Due to the current developments towards autonomous driving and vehicle active safety, there is an increasing necessity for algorithms that are able to perform complex criticality predictions in real-time. Being able to process multi-object traffic scenarios aids the implementation of a variety of automotive applications such as driver assistance systems for collision prevention and mitigation as well as fall-back systems for autonomous vehicles. We present a fully model-based algorithm with a parallelizable architecture. The proposed algorithm can evaluate the criticality of complex, multi-modal (vehicles and pedestrians) traffic scenarios by simulating millions of trajectory combinations and detecting collisions between objects. The algorithm is able to estimate upcoming criticality at very early stages, demonstrating its potential for vehicle safety-systems and autonomous driving applications. An implementation on an embedded system in a test vehicle proves in a prototypical manner the compatibility of the algorithm with the hardware possibilities of modern cars. For a complex traffic scenario with 11 dynamic objects, more than 86 million pose combinations are evaluated in 21 ms on the GPU of a Drive PX~2

Context-aware system for pre-triggering irreversible vehicle safety actuators

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.New vehicle safety systems have led to a steady improvement of road safety and a reduction in the risk of suffering a major injury in vehicle accidents. A huge leap forward in the development of new vehicle safety systems are actuators that have to be activated irreversibly shortly before a collision in order to mitigate accident consequences. The triggering decision has to be based on measurements of exteroceptive sensors currently used in driver assistance systems. This paper focuses on developing a novel context-aware system designed to detect potential collisions and to trigger safety actuators even before an accident occurs. In this context, the analysis examines the information that can be collected from exteroceptive sensors (pre-crash data) to predict a certain collision and its severity to decide whether a triggering is entitled or not. A five-layer context-aware architecture is presented, that is able to collect contextual information about the vehicle environment and the actual driving state using different sensors, to perform reasoning about potential collisions, and to trigger safety functions upon that information. Accident analysis is used in a data model to represent uncertain knowledge and to perform reasoning. A simulation concept based on real accident data is introduced to evaluate the presented system concept