Enhancing motion safety by identifying safety-critical passageways

Abstract

Abstract-Safety is the most important aspect of systems which have to perform collision-free motions in dynamic environments. Formal verification methods, such as reachability analysis, are capable of guaranteeing safety for a given model and given assumptions (e. g. bounded velocity and acceleration). However, certain assumptions can be violated by dynamic obstacles during the execution of the verified motion plan, exposing the system to potential collisions. To compensate for the invalidated verification, this paper introduces the Point of No Return (PNR) and the Point of Guaranteed Arrival (PGA) by incorporating invariably safe sets. These concepts allow one to divide the planned trajectory into safe sections and safety-critical passageways. For the former, we are able to provide safety guarantees for an infinite time horizon. For the latter, we present a method to minimize such safety-critical passageways prior to execution and thus reduce the risk of potential collisions if assumptions are violated during execution. The safety benefits are highlighted by a numerical example of overtaking maneuvers of self-driving vehicles.

Efficient Computation of Invariably Safe States for Motion Planning of Self-Driving Vehicles

“…This paper formally defines and computes invariably safe sets, which are regions in which vehicles are able to remain safe for an infinite time horizon. This concretizes our previous work [23] for a lane-based setting, which considers arbitrary traffic scenarios. In contrast to computationally expensive approaches (cf.…”

Set-Based Prediction of Traffic Participants Considering Occlusions and Traffic Rules

“…Since the mean values of all violations are only slightly above the initial values, the initial parameterization seems reasonable, but can be adjusted to user preferences. To reduce the influence of violated constraints on the safety of motion plans, we refer to [11]. Legal safety can be ensured despite constraint violations by planning fail-safe trajectories [15] and switching to a reactive mode for collision mitigation in case of inevitable collisions.…”

“…In [8], we have shown that this does not result in overly conservative behaviors for the ego vehicle. Our proposed method has several applications for autonomous vehicles and driver assistance systems: a) Safe states: Based on the predicted occupancies, we can determine the maximum drivable area [9], the maximum Time-To-React [10], and the Point of No Return [11]. By additionally considering the predicted velocity, we can compute safe states for the ego vehicle, e. g., to maintain a safe distance to other vehicles [12].…”

Subjective logic reasoning: an urn model intuition and application to connected automated driving

“…Motion planning under uncertainties so far has been addressed through classical approaches [1,4,30,37,38], set-based methods [17,25,27,28], Markov Decision Processes (MDP) [9,34], and communication based approaches [33]. None of these works, however, accounts for the possibility of having an unreliable environmental model.…”