Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance
This work addresses the need for early criticality estimation in complex traffic environments to enhance collision avoidance and safety for autonomous vehicles and driver assistance systems, though it appears incremental as it builds on model-based simulation approaches.
The authors tackled the problem of real-time criticality prediction in multi-object traffic scenarios for autonomous driving and vehicle safety systems, achieving the evaluation of over 86 million pose combinations in 21 ms on a GPU for a scenario with 11 dynamic objects.
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.