Abstract:
Navigation in congested environments is a challenge for autonomous vehicles and they should consider collision risk metric into their driving behavior. In this paper, we ...Show MoreMetadata
Abstract:
Navigation in congested environments is a challenge for autonomous vehicles and they should consider collision risk metric into their driving behavior. In this paper, we propose a novel two-fold indicator: On the one hand, single-shot risk works in space domain, considering geometries, locations and the velocities of the obstacles in the current scene. On the other hand, long-term risk considers the evolution of the current scene and provides risk values in time domain. The map information and different prediction models (e.g. reachable sets, probabilistic) are considered in the long-term risk, which can then be used in trajectory planning or decision making approaches. Our method can be applied to scenarios with arbitrary road topologies (intersections, roundabouts, highway, etc.) and it is suitable regardless of the scene prediction method. We formulate the single-shot (or short-term) risk with one single function fitted using Monte Carlo (MC) Simulations. The results are evaluated in real scenarios using HighD dataset and compared with other risk indicators such as THW and TTC. In addition, it is applied to a simple trajectory planner in order to demonstrate that the proposed approach imitates human driving style.
Published in: 2020 IEEE Intelligent Vehicles Symposium (IV)
Date of Conference: 19 October 2020 - 13 November 2020
Date Added to IEEE Xplore: 08 January 2021
ISBN Information: