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

Abstract

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 multiobject 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.

Pedestrian Collision Avoidance in Autonomous Vehicles: A Review

“…Table 5 lists studies in the literature that investigated model-based approaches. [13,15,17,18,22,23,25,40,44,45] As mentioned in [15], the advantage of this type of approach in comparison to learningbased approaches is that these approaches do not require the construction of a reliable training dataset, which may struggle to cover all PCA scenarios. In addition, the solutions that are developed on the basis of a mathematical model are optimal.…”

“…List of works that used a real car for the testing of PCA systems that they proposed. [9,15,19,20,33] In general, the proposed approaches in these works were not tested on a real vehicle as the first stage of testing but rather underwent tests in simulators before being tested on real vehicles. Deploying a PCA approach directly (i.e., without first testing it in simulation) on a real-life AV for tests in real traffic could entail risks for the pedestrians involved in the tests as well as for the equipment used, even if there is usually a safety driver in the vehicles.…”

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

“…Similar to other probability-based approaches, Sánchez Morales et al propose to assign probabilities to predicted trajectories and accumulate them into a collision probability [67]. The ego's motion is modeled by a two track model, cf.…”

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

“…Similar to other probability-based approaches, Sánchez Morales et al propose to assign probabilities to predicted trajectories and accumulate them into a collision probability [68]. The ego's motion is modeled by a two track model, cf.…”