Safe Speed Control and Collision Probability Estimation Under Ego-Pose Uncertainty for Autonomous Vehicle

Abstract

In order for autonomous vehicles to become a part of the Intelligent Transportation Ecosystem, they are required to guarantee a particular level of safety. For that to happen a safe vehicle control algorithms need to be developed, which include assessing the probability of a collision while driving along a given trajectory and selecting control signals that minimize this probability. In this paper, we propose a speed control system that estimates a collision probability taking into account static and dynamic obstacles as well as ego-pose uncertainty and chooses the maximum safe speed. For that, the planned trajectory is converted by the control system into control signals that form input for the dynamic vehicle model. The model predicts a real vehicle path. The predicted trajectory is generated for each particle -a weighted by a probability hypothesis of the localization system about the vehicle pose. Based on the predicted particles' trajectories, the probability of collision is calculated, and a decision is made on the maximum safe speed. The proposed algorithm was validated on the real autonomous vehicle. The experimental results demonstrate that the proposed speed control system reduces the vehicle speed to a safe value when performing maneuvers and driving through narrow openings. Therefore the observed behavior of the system is mimicking a human driver behavior when driving in difficult and ambiguous traffic situations.

Collision Mitigation in the Presence of Uncertainty

“…The results for trajectories 3, 7, 5 and 10 are shown in Fig. (9). By reducing the prediction time, for trajectory 3 and 7 we did not see any large variation in the severity of collision.…”

“…B, and only examples of some of these trajectories (1, 3, 4, 5, 7, 10) are used and presented here. As mentioned in Section IV the collision probability threshold and the number of required ego vehicle trajectories (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) to reach a threshold are varied. Therefore, the earliest activation of the CMS is when only one trajectory of the ego vehicle has reached 10% collision probability, and the latest is when all trajectories of the ego vehicle have reached 98% collision probability.…”

“…Similarly, reducing the prediction horizon from 1s to 0.8s improved the prediction accuracy and reduced severity in cases such as trajectory 5 and 10 in Fig. (9). However, it also provided the ego vehicle with less flexibility to reduce collision severity in other cases such as trajectory 3 and 7 in Fig.…”

“…However, it also provided the ego vehicle with less flexibility to reduce collision severity in other cases such as trajectory 3 and 7 in Fig. (9).…”

“…In [8], a probabilistic collision mitigation velocity controller is proposed, where an acceleration input with a lower probability is prioritized over one with a higher probability. In [9], a safe speed control system for autonomous vehicles is introduced based on collision probability estimations that also considers the uncertain position of the ego vehicle. Predictions for each pose hypothesis of the ego vehicle is made and the optimal speed is calculated from collision probability.…”