Computing possible driving corridors for automated vehicles

Abstract

Motion planning in dynamic traffic scenes is a challenging problem. In particular, since it is unknown during planning whether a certain decision, such as passing another traffic participant on the left or right, will result in a safe and comfortable motion. Exhaustive exploration of all principle driving paths is computationally expensive, so that one typically reverts to heuristics-this, however can be unsatisfactory in situations when the heuristics fail to find a solution although it exists. We address this problem by computing the union of all possible motions for a sequence of high-level decisions (e.g. overtake vehicle on the left and then another one on the right), which we refer to as a driving corridor. Our proposed algorithm is over-approximative, i.e. the union of driving corridors provably encloses all possible motions. Thus, if the set of reachable positions within a driving corridor becomes empty, the corresponding sequence of high-level decisions is infeasible and can be discarded by the motion planner. Driving corridors also facilitate selecting high-level plans: Large driving corridors should be preferred since they provide more opportunities for optimizing motions and are more robust towards unpredicted changes. Numerical examples demonstrate the usefulness of our approach.

Automatic Generation of Safety-Critical Test Scenarios for Collision Avoidance of Road Vehicles

“…We compute the drivable area D(t; X 0 , W free (•), t h ) according to the approach presented in [41]. To efficiently compute the drivable area, it is represented by the union of Cartesian products of convex polytopes.…”

“…3 of an overtaking scenario for different time steps. For a more detailed description, the reader is referred to [41]. The drivable area is used in the optimization routine below to generate scenarios which have a minimum weighted deviation to a desired drivable area according to (2).…”

Visualizing Local Minima in Multi-Robot Motion Planning using Multilevel Morse Theory

“…First, we often missed local minima when the configuration space contained narrow passages. We could alleviate this problem by biasing sampling towards narrow passages, by analyzing locally reachable sets [25] or by targeted sampling of undiscovered braid pattern [3]. Second, the algorithm can return minima with cycling behavior, where two robots cycle around each other before continuing.…”

“…By visualizing local minima, we allow interaction by non-expert users, to either guide or prevent motions [17]. By visualizing local minima, we can create high-level options [18], usable to make high-level decisions [25] or perform rapid re-planning [32].…”

Using Reachable Sets for Trajectory Planning of Automated Vehicles

“…Reachable sets have already been used to obtain driving corridors and to determine the non-existence of maneuvers [9], [54], [55]. The approaches in [56]- [58] combine reachability analysis with planning approaches to obtain collision-free motions.…”