Safe Stop Trajectory Planning for Highly Automated Vehicles: An Optimal Control Problem Formulation

Abstract

Highly automated road vehicles need the capability of stopping safely in a situation that disrupts continued normal operation, e.g. due to internal system faults. Motion planning for safe stop differs from nominal motion planning, since there is not a specific goal location. Rather, the desired behavior is that the vehicle should reach a stopped state, preferably outside of active lanes. Also, the functionality to stop safely needs to be of high integrity. The first contribution of this paper is to formulate the safe stop problem as a benchmark optimal control problem, which can be solved by dynamic programming. However, this solution method cannot be used in real-time. The second contribution is to develop a real-time safe stop trajectory planning algorithm, based on selection from a precomputed set of trajectories. By exploiting the particular properties of the safe stop problem, the cardinality of the set is decreased, making the algorithm computationally efficient. Furthermore, a monitoring based architecture concept is proposed, that ensures dependability of the safe stop function. Finally, a proof of concept simulation using the proposed architecture and the safe stop trajectory planner is presented.

Legal issues in automated vehicles: critically considering the potential role of consent and interactive digital interfaces

“…Systems monitoring heart rate, pupil diameter and gaze may be able to detect when a person is not able to respond to a takeover situation (Alrefaie et al, 2019). These systems could potentially work in conjunction with an emergency fall back of safe stop or safe manoeuvrer, where the driver is not ready (Svensson, 2018). However, even where drivers are alerted to a vehicle handover via auditory/visual and haptic warning messages, drivers struggle to regain attention to driving following a period of automated driving (Bloomberg, 2017).…”

Adaptive Trajectory Planning and optimization at Limits of Handling

“…An intuitive way to reduce suboptimality of the trajectories in the set is to solve the two-point boundary value problem offline, using a dynamic model. This method has been demonstrated successfully in several previous works [5], [6]. However, this approach prohibits online model adaptation, since the trajectories in the pre-computed library are computed based on a static vehicle representation.…”

“…The non-adaptive trajectory optimization assumes a static friction estimate µ asm = 0.8 throughout, while our proposed SAA-RTI reestimates this value and accordingly adapts input constraints U t (µ t ) (see Fig. 1) in (5). To mimic the convergence time of a friction estimation algorithm, we introduce a time delay of 100ms before the correct value of µ is applied in SAA-RTI.…”

The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections

“…For road vehicles, Werling et al [34] proposed an optimal trajectory generation method, with which velocity maintenance, merging, following, stopping and a reactive collision avoidance functionality were achieved. In [35] a real-time safe stop trajectory planning algorithm, based on selection from a precomputed set of trajectories has been developed. Several papers deal with the selection of the velocity of autonomous vehicles, which has relevance in the problem of intersection scenarios.…”