Ensuring the safety of autonomous vehicles (AV s) in uncertain traffic scenarios is a major challenge. In this paper, we address the problem of computing the risk that AV s violate a given safety specification in uncertain traffic scenarios, where state estimates are not perfect. We propose a risk measure that captures the probability of violating the specification and determines the average expected severity of violation. Using highway scenarios of the US101 dataset and Responsible Sensitive Safety (RSS) as an example specification, we demonstrate the effectiveness and benefits of our proposed risk measure. By incorporating the risk measure into a trajectory planner, we enable AVs to plan minimal-risk trajectories and to quantify trade-offs between risk and progress in traffic scenarios.

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 trajectories. Fail-safe trajectories are executed if the intended motion of the autonomous vehicle causes a safety-critical situation. Our verification technique is real-time capable and operates under the premise that intended trajectories are only executed if they have been verified as safe. The benefits of our proposed approach are demonstrated in different scenarios on an actual vehicle. Moreover, we present the first in-depth analysis of our verification technique used in dense urban traffic. Our results indicate that fail-safe motion planning has the potential to drastically reduce accidents while not resulting in overly conservative behaviors of the autonomous vehicle.

Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the passenger. We present a new approach to encode human driving styles through the use of signal temporal logic and its robustness metrics. Specifically, we use a penalty structure that can be used in many motion planning frameworks, and calibrate its parameters to model different automated driving styles. We combine this penalty structure with a set of signal temporal logic formula, based on the Responsibility-Sensitive Safety model, to generate trajectories that we expected to correlate with three different driving styles: aggressive, neutral, and defensive. An online study showed that people perceived different parameterizations of the motion planner as unique driving styles, and that most people tend to prefer a more defensive automated driving style, which correlated to their self-reported driving style.

Visual planning approaches have shown great success for decision making tasks with no explicit model of the state space. Learning a suitable representation and constructing a latent space where planning can be performed allows non-experts to setup and plan motions by just providing images. However, learned latent spaces are usually not semantically-interpretable, and thus it is difficult to integrate task constraints. We propose a novel framework to determine whether plans satisfy constraints given demonstrations of policies that satisfy or violate the constraints. The demonstrations are realizations of Linear Temporal Logic formulas which are employed to train Long Short-Term Memory (LSTM) networks directly in the latent space representation. We demonstrate that our architecture enables designers to easily specify, compose and integrate task constraints and achieves high performance in terms of accuracy. Furthermore, this visual planning framework enables human interaction, coping the environment changes that a human worker may involve. We show the flexibility of the method on a box pushing task in a simulated warehouse setting with different task constraints.

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 Drivability Checker - an open-source toolbox - unifies these mentioned checks. It is compatible with the CommonRoad benchmark suite, which additionally facilitates the development of motion planners. Our toolbox drastically reduces the effort of developing and validating motion planning algorithms. Numerical experiments show that our toolbox is real-time capable and can be used in real test vehicles.

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 participants are allowed to perform any behaviour in accordance with traffic rules. Our technique serves as a safety layer for existing motion planning frameworks that provide intended trajectories for autonomous vehicles. We verify whether intended trajectories comply with legal safety and provide fallback solutions in safety-critical situations. The benefits of our verification technique are demonstrated in critical urban scenarios, which have been recorded in real traffic. The autonomous vehicle executed only safe trajectories, even when using an intended trajectory planner that was not aware of other traffic participants. Our results indicate that our online verification technique can drastically reduce the number of traffic accidents.

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 the cooperative vehicles remain safe for an infinite time horizon. The potential conflicts between the ISSs of a group of cooperative vehicles are resolved by examining and negotiating their Safe Maneuver Corridors. As a result, each vehicle obtains its negotiated ISS, which is used as target sets for motion planning. We demonstrate the applicability and benefits of our method on various traffic scenarios from the CommonRoad benchmark suite.