While motion planning approaches for automated driving often focus on safety and mathematical optimality with respect to technical parameters, they barely consider convenience, perceived safety for the passenger and comprehensibility for other traffic participants. For automated driving in mixed traffic, however, this is key to reach public acceptance. In this paper, we revise the problem statement of motion planning in mixed traffic: Instead of largely simplifying the motion planning problem to a convex optimization problem, we keep a more complex probabilistic multi agent model and strive for a near optimal solution. We assume cooperation of other traffic participants, yet being aware of violations of this assumption. This approach yields solutions that are provably safe in all situations, and convenient and comprehensible in situations that are also unambiguous for humans. Thus, it outperforms existing approaches in mixed traffic scenarios, as we show in our simulation environment.
@cite_2 also model the problem as a single agent MDP. They do not assume deterministic state transitions regarding the state of other traffic participants. Instead, they focus on binary decisions on complementary events. These result from two maneuver options of the other vehicle, for example whether to yield or not at an intersection. The probabilities are computed using a logistic regression model. For every option, the future trajectory of the ego vehicle is computed using the approach of @cite_0 . The motion plan for either option has to be equal for a certain (short) time horizon as long as the probability does not equal zero for one of the options. This corresponds to postponing the decision until one option has probability zero. Given the probabilities, the expected finite horizon costs are then calculated using the mean of the costs for the two plans, weighted with their probability. The optimal plan is found via minimizing the expected finite horizon costs. While this approach is safe, assuming that probability zero equals physical infeasibility of the option, it does also not account for the fact that the ego motion affects the motion of other traffic participants.
@cite_0 @cite_2
2148361676 2562817597
In this paper, we present the strategy for trajectory planning that was used on-board the vehicle that completed the 103 km of the Bertha-Benz-Memorial-Route fully autonomously. We suggest a local, continuous method that is derived from a variational formulation. The solution trajectory is the constrained extremum of an objective function that is designed to express dynamic feasibility and comfort. Static and dynamic obstacle constraints are incorporated in the form of polygons. The constraints are carefully designed to ensure that the solution converges to a single, global optimum. 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.