Ensuring the safety of autonomous vehicles (AVs) in uncertain traffic scenarios is a major challenge. In this paper, we address the problem of computing the risk that AVs 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.

Motion planning in uncertain environments like complex urban areas is a key challenge for autonomous vehicles (AVs). The aim of our research is to investigate how AVs can navigate crowded, unpredictable scenarios with multiple pedestrians while maintaining a safe and efficient vehicle behavior. So far, most research has concentrated on static or deterministic traffic participant behavior. This paper introduces a novel algorithm for motion planning in crowded spaces by combining social force principles for simulating realistic pedestrian behavior with a risk-aware motion planner.

We evaluate this new algorithm in a 2D simulation environment to rigorously assess AV-pedestrian interactions, demonstrating that our algorithm enables safe, efficient, and adaptive motion planning, particularly in highly crowded urban environments—a first in achieving this level of performance. This study has not taken into consideration real-time constraints and has been shown only in simulation so far. Further studies are needed to investigate the novel algorithm in a complete software stack for AVs on real cars to investigate the entire perception, planning and control pipeline in crowded scenarios. We release the code developed in this research as an open-source resource for further studies and development. It can be accessed at the following link: https://github.com/TUM-AVS/PedestrianAwareMotionPlanning.

Safe driving requires autonomous vehicles to anticipate potential hidden traffic participants and other unseen objects, such as a cyclist hidden behind a large vehicle, or an object on the road hidden behind a building. Existing methods are usually unable to consider all possible shapes and orientations of such obstacles. They also typically do not reason about observations of hidden obstacles over time, leading to conservative anticipations. We overcome these limitations by (1) modeling possible hidden obstacles as a set of states of a point mass model and (2) sequential reasoning based on reachability analysis and previous observations. Based on (1), our method is safer, since we anticipate obstacles of arbitrary unknown shapes and orientations. In addition, (2) increases the available drivable space when planning trajectories for autonomous vehicles. In our experiments, we demonstrate that our method, at no expense of safety, gives rise to significant reductions in time to traverse various intersection scenarios from the CommonRoad Benchmark Suite.

Limited visibility and sensor occlusions pose pressing safety challenges for advanced driver-assistance systems (ADAS) and autonomous vehicles (AVs). In this work, our pursuit was to strike a balance: a method that ensures safety in occluded scenarios while preventing overly cautious behavior. We argue that such approaches are crucial for AVs' future, particularly when navigating alongside human drivers on highways at high speeds. To this end, we used reachability analysis to find safe velocity bounds on occluded traffic participants. Compared to state-of-the-art methods, we achieved velocity increases in more than 60% of the 230 cut-in scenarios from the highD dataset, without sacrificing safety.

Vehicle-to-everything (V2X) communication holds significant promise for augmenting autonomous driving capabilities. Particularly in dense traffic with occluded areas, V2X can be used to share information about the respective observed areas between traffic participants. In turn, reducing uncertainty about unseen areas can lead to less conservative behaviors while maintaining collision avoidance.This paper aims to leverage V2X to improve situation awareness for trajectory planning. We particularly address two challenges: First, the ego vehicle may not always receive up-to-date information. Second, some areas may remain occluded despite receiving information from other participants.In this work, we fuse the received information about the detected free space. We use reachability analysis to compute areas that are guaranteed to be free despite being occluded. This way, we can maintain collision-avoidance guarantees. We demonstrate the benefits of our proposed method both in simulations and physical experiments.

The deployment of autonomous vehicles in urban environments introduces significant safety challenges, particularly in scenarios with occlusions, where critical traffic participants may be hidden from view. Recent accidents involving driverless vehicles highlight the importance of motion planners that explicitly addresses the risks posed by occlusions.

In this work, we propose a formal, occlusion-aware trajectory planning framework that guarantees collision avoidance even when there are possible hidden traffic participants. Building on our previous methods that apply reachability analysis to sequentially determine the possible states of hidden traffic participants, we integrate a tree-based motion planner capable of reasoning over future observations and the absence thereof. This approach reduces conservativeness while maintaining safety guarantees.

We demonstrate the effectiveness of our framework in a challenging simulated occluded scenario, showing that it pro-actively and efficiently guarantees collision-avoidance.