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Conference Papers Year : 2021

Real-time Collision Risk Estimation based on Stochastic Reachability Spaces


Estimating the risk of collision with other road users is one of the most important modules to ensure safety in autonomous driving scenarios. In this paper, we propose new probabilistic models to obtain Stochastic Reachability Spaces for vehicles and pedestrians detected in the scene. We then exploit these probabilistic predictions of the road-users' future positions, along with the expected ego-vehicle trajectory, to estimate the probability of collision risk in real-time. The proposed stochastic models only depend on the velocity, acceleration, tracked bounding box, and the class of the detected object. This information can easily be obtained through off-the-shelf 3D object detection frameworks. As a result, the proposed approach for collision risk estimation is widely applicable to a variety of autonomous vehicle platforms. To validate our approach, initially we test the stochastic motion prediction on the KITTI dataset. Further experiments in the CARLA simulator, by reproducing realistic collision scenarios, have the goal of demonstrating the effectiveness of the collision risk assessment and are compared with an alternative approach.
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hal-03416222 , version 1 (05-11-2021)



Unmesh Patil, Alessandro Renzaglia, Anshul Paigwar, Christian Laugier. Real-time Collision Risk Estimation based on Stochastic Reachability Spaces. ICAR 2021 - International Conference on Advanced Robotics, Dec 2021, Ljubljana, Slovenia. pp.1-6, ⟨10.1109/ICAR53236.2021.9659485⟩. ⟨hal-03416222⟩
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