MA
Matthias Althoff
Authored
16 records found
CommonRoad Drivability Checker
Simplifying the Development and Validation of Motion Planning Algorithms
Collision avoidance, kinematic feasibility, and road-compliance must be validated to ensure the drivability of planned motions for autonomous vehicles. Although these tasks are highly repetitive, computationally efficient toolboxes are still unavailable. The CommonRoad Drivabilit
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We propose a counterexample-guided inductive synthesis framework for the formal synthesis of closed-form sampled-data controllers for nonlinear systems to meet STL specifications over finite-time trajectories. Rather than stating the STL specification for a single initial conditi
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Adaptive cruise control is one of the most common comfort features of road vehicles. Despite its large market penetration, current systems are not safe in all driving conditions and require supervision by human drivers. While several previous works have proposed solutions for saf
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The computational effort of trajectory planning for automated vehicles often increases with the complexity of the traffic situation. This is particularly problematic in safety-critical situations, in which the vehicle must react in a timely manner. We present a novel motion plann
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Safe motion planning for autonomous vehicles is a challenging task, since the exact future motion of other traffic participant is usually unknown. In this article, we present a verification technique ensuring that autonomous vehicles do not cause collisions by using fail-safe tra
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Falsification aims to disprove the safety of systems by providing counter-examples that lead to a violation of safety properties. In this work, we present two novel falsification methods to reveal safety flaws in adaptive cruise control (ACC) systems of automated vehicles. Our me
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Ensuring that autonomous vehicles do not cause accidents remains a challenge. We present a formal verification technique for guaranteeing legal safety in arbitrary urban traffic situations. Legal safety means that autonomous vehicles never cause accidents although other traffic p
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Self-driving vehicles must be able to safely navigate in any traffic scenario. However, all situations are different; even when clustering them, an impractical amount of scenarios would have to be verified. Thus, we propose a safety framework to verify the safety of each planned
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Validating the safety of self-driving vehicles requires an enormous amount of testing. By applying formal verification methods, we can prove the correctness of the vehicles' behavior, which at the same time reduces remaining risks and the need for extensive testing. However, curr
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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
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Set-based predictions can ensure the safety of planned motions, since they provide a bounded region which includes all possible future states of nondeterministic models of other traffic participants. However, while autonomous vehicles are tested in urban environments, a set-based
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We address the problem of provably-safe cooperative driving for a group of vehicles that operate in mixed traffic scenarios, where both autonomous and human-driven vehicles are present. Our method is based on Invariably Safe Sets (ISSs), which are sets of states that let each of
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Machine learning techniques have been shown to outperform many rule-based systems for the decision-making of autonomous vehicles. However, applying machine learning is challenging due to the possibility of executing unsafe actions and slow learning rates. We address these issues
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Ensuring the safety of self-driving vehicles is a challenging task, especially if other traffic participants severely deviate from the predicted behavior. One solution is to ensure that the vehicle is able to execute a collision-free evasive trajectory at any time. However, a fas
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The application of continuous optimization to motion planning of autonomous vehicles has enjoyed increasing popularity in recent years. In order to maintain low computation times, it is advantageous to have a convex formulation, in general requiring the planning problem to be sep
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Safe motion planning requires that a vehicle reaches a set of safe states at the end of the planning horizon. However, safe states of vehicles have not yet been systematically defined in the literature, nor does a computationally efficient way to obtain them for online motion pla
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