As deployment of automated vehicles increases, so does the rate at which they are exposed to critical traffic situations. Such situations, e.g. a late detected pedestrian in the vehicle path, require operation at the handling limits in order to maximize the capacity to avoid an accident. Also, the physical limitations of the vehicle typically vary in time due to local road and weather conditions. In this paper, we tackle the problem of trajectory planning and control at the limits of handling under time varying constraints, by adapting to local traction limitations. The proposed method is based on Real Time Iteration Sequential Quadratic Programming (RTI-SQP) augmented with state space sampling, which we call Sampling Augmented Adaptive RTI-SQP (SAA-SQP). Through extensive numerical simulations we demonstrate that our method increases the vehicle's capacity to avoid late detected obstacles compared to the traditional planning tracking approaches, as a direct consequence of safe operating constraint adaptation in real time.
Hence for practicality, state space sampling methods such as those presented by @cite_29 are widely used in industry for collision avoidance. The core concept of the method is as follows. A grid is defined in the terminal state of the planning horizon and a set of two point boundary value problems are solved between the initial state and each sampled terminal state, generating a trajectory set. Dynamic constraints are not considered in the generation of the trajectory set. Instead, a dynamic feasibility check is done in conjunction with the collision check for each trajectory. extended the method in @cite_9 by generating the trajectory set in a road-aligned coordinate frame, @cite_17 , and in @cite_22 by introducing a terminal manifold to improve the selection of terminal states. It has been shown that the method is well suited for planning in scenarios including discrete decisions. However, even though it reliably produces feasible maneuvers, they are suboptimal w.r.t. the physical capabilities of the vehicle.
@cite_9 @cite_29 @cite_22 @cite_17
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Safe handling of dynamic highway and inner city scenarios with autonomous vehicles involves the problem of generating traffic-adapted trajectories. In order to account for the practical requirements of the holistic autonomous system, we propose a semi-reactive trajectory generation method, which can be tightly integrated into the behavioral layer. The method realizes long-term objectives such as velocity keeping, merging, following, stopping, in combination with a reactive collision avoidance by means of optimal-control strategies within the Frenet-Frame [12] of the street. The capabilities of this approach are demonstrated in the simulation of a typical high-speed highway scenario. Sampling in the space of controls or actions is a well-established method for ensuring feasible local motion plans. However, as mobile robots advance in performance and competence in complex environments, this classical motion-planning technique ceases to be effective. When environmental constraints severely limit the space of acceptable motions or when global motion planning expresses strong preferences, a state space sampling strategy is more effective. Although this has been evident for some time, the practical question is how to achieve it while also satisfying the severe constraints of vehicle dynamic feasibility. The paper presents an effective algorithm for state space sampling utilizing a model-based trajectory generation approach. This method enables high-speed navigation in highly constrained and-or partially known environments such as trails, roadways, and dense off-road obstacle fields. © 2008 Wiley Periodicals, Inc. 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. Through two different approaches, this report proposes two general controllers for unicycle-type and two-steering-wheels mobile robots. For both systems, conditions for asymptotical convergence to a predefined path are established and simulation results are presented. Rather than writing the systems' equations with respect to a fixed reference frame, the robot state is here parametrized relative to the followed path, in terms of distance and orientation.