Optimal trajectories for time-critical street scenarios using discretized terminal manifolds

Abstract

This paper deals with the trajectory generation problem faced by an autonomous vehicle in moving traffic. Being given the predicted motion of the traffic flow, the proposed semi-reactive planning strategy realizes all required long-term maneuver tasks (lane-changing, merging, distance-keeping, velocity-keeping, precise stopping, etc.) while providing short-term collision avoidance. The key to comfortable, human-like as well as physically feasible trajectories is the combined optimization of the lateral and longitudinal movements in street-relative coordinates with carefully chosen cost functionals and terminal state sets (manifolds). The performance of the approach is demonstrated in simulated traffic scenarios.

Interaction aware trajectory planning for merge scenarios in congested traffic situations

“…Early work included rule-based finite state machines that considered conservative time gaps and critical sections but they quickly reached their limits when more complex traffic situations were considered [5], [6], [1]. Werling et al [7] presented a trajectory based planning approach capable of handling more complex situations by calculating a set of candidate trajectories to a discretized terminal manifold and then selecting the most suitable trajectory within the set. The method used a short planning horizon of 3 s and handed the long term safety requirement of the vehicle to another decision layer on top making it unsuitable for safe merging where longer horizons are needed.…”

“…However, we extend the model to include information of the underlying road network making it possible to handle interaction at intersections. We then generate a vast set of minimal jerk velocity profiles in a similar way as in [7] that are checked against the physical constraints of the ego vehicle and then fed to a prediction engine where the traffic development due to our action is predicted. Dangerous velocity profiles are then removed by checking constraints on the induced acceleration needed by other traffic participants to avoid a collision.…”

“…To generate the candidate velocity profiles, minimal jerk solutions are calculated in a similar way as in [7]. The longitudinal movement of the ego vehicle is modeled as a double integrator system with states, x = [s, v, a] T where s is the traveled arc-length, v is the velocity and a is the acceleration of the ego vehicle.…”

Fail-safe motion planning of autonomous vehicles

“…: grid-based approaches [15], planning using motion primitives [1], rapidly-exploring random trees [2]- [4], and road maps [5]- [9]); 2) planning in continuous space (e.g. optimal control, model predictive control [10]- [13], and elastic bands [14] for collision-free trajectory planning for mobile robots can be found in [16].…”

“…For optimal trajectories which consider constraints, optimal control or MPC is used [10]- [13]. In [10], MPC is utilized for trajectory planning to prevent lane departure.…”