Real-time safety systems are crucial components of intelligent vehicles. This paper introduces a prediction-based collision risk assessment approach on highways. Given a point mass vehicle dynamics system, a stochastic forward reachable set considering two-dimensional motion with vehicle state probability distributions is firstly established. We then develop an acceleration prediction model, which provides multi-modal probabilistic acceleration distributions to propagate vehicle states. The collision probability is calculated by summing up the probabilities of the states where two vehicles spatially overlap. Simulation results show that the prediction model has superior performance in terms of vehicle motion position errors, and the proposed collision detection approach is agile and effective to identify the collision in cut-in crash events.
|Title of host publication||Proceedings of the 2022 IEEE Intelligent Vehicles Symposium (IV)|
|Publication status||Published - 2022|
|Event||2022 IEEE Intelligent Vehicles Symposium (IV) - Aachen, Germany|
Duration: 5 Jun 2022 → 9 Jun 2022
|Conference||2022 IEEE Intelligent Vehicles Symposium (IV)|
|Period||5/06/22 → 9/06/22|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Road transportation
- Intelligent vehicles
- Stochastic processes
- Predictive models
- Probability distribution
- Risk management