A non conservatively defensive strategy for urban autonomous driving
From the driving strategy point of view, a major challenge for autonomous vehicles in urban environment is to behave defensively to potential dangers, yet to not overreact to threats with low probability. As it is overwhelming to program the action rules case by case, a unified planning framework under uncertainty is proposed in this paper, which achieves a non conservatively defensive strategy (NCDS) in various kinds of scenarios for urban autonomous driving. First, uncertainties in urban scenarios are simplified to two probabilistic cases, namely passing and yielding. Two way stop intersection is used as an exemplar scenario to illustrate the derivation of probabilities for different intentions of others via a logistic regression model. Then a deterministic planner is designed as the baseline. Also, a safe set is defined, which considers both current and preview safety. The planning framework under uncertainty is then proposed, in which safety is guaranteed and overcautious behavior is prevented. Finally, the proposed planning framework is tested by simulation in the exemplar scenario, which demonstrates that an NCDS can be realistically achieved by employing the proposed framework.
2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)