TY - CHAP A1 - Tolksdorf, Leon A1 - Birkner, Christian A1 - Tejada, Arturo A1 - Van De Wouw, Nathan T1 - Fast Collision Probability Estimation for Automated Driving using Multi-circular Shape Approximations T2 - 2024 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV55156.2024.10588731 Y1 - 2024 UR - https://doi.org/10.1109/IV55156.2024.10588731 SN - 979-8-3503-4881-1 SP - 2529 EP - 2536 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Tolksdorf, Leon A1 - Tejada, Arturo A1 - van de Wouw, Nathan A1 - Birkner, Christian T1 - Risk in Stochastic and Robust Model Predictive Path-Following Control for Vehicular Motion Planning T2 - IEEE IV 2023: Symposium Proceedings UR - https://doi.org/10.1109/IV55152.2023.10186708 KW - autonomous vehicles KW - motion planning KW - path following KW - robust model predictive control KW - stochastic model predictive control KW - risk assessment Y1 - 2023 UR - https://doi.org/10.1109/IV55152.2023.10186708 SN - 979-8-3503-4691-6 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Weihmayr, Daniel A1 - Sezgin, Fatih A1 - Tolksdorf, Leon A1 - Birkner, Christian A1 - Jazar, Reza T1 - Predicting the Influence of Adverse Weather on Pedestrian Detection with Automotive Radar and Lidar Sensors T2 - 2024 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV55156.2024.10588472 Y1 - 2024 UR - https://doi.org/10.1109/IV55156.2024.10588472 SN - 979-8-3503-4881-1 SP - 2591 EP - 2597 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Tolksdorf, Leon A1 - Tejada, Arturo A1 - Bauernfeind, Jonas A1 - Birkner, Christian A1 - van de Wouw, Nathan T1 - Risk Estimation for Automated Driving UR - https://doi.org/10.48550/arXiv.2601.15018 Y1 - 2026 UR - https://doi.org/10.48550/arXiv.2601.15018 PB - arXiv CY - Ithaca ER - TY - THES A1 - Tolksdorf, Leon T1 - Risk-Based Motion Planning in Automated Vehicles N2 - It is anticipated that there will be a long period where automated vehicles (AVs) and human road users share the same road network, with limited communication between them. As a result, AVs often operate with uncertain knowledge of their surroundings due to limitations in both sensing and estimation of the traffic scenario's current and future states. These limitations can be caused by factors such as noisy sensor data or lack of information about the intentions of other road users. Accounting for this uncertainty while designing motion planning and control algorithms for AVs is critical, as an AV may display hazardous behavior if it is overly confident in falsely estimated environment states or, contrarily, may act too cautiously when considering unnecessarily large safety margins in estimated states. Although safety regulations call for taking uncertainty into account, the right means to integrate uncertainty in motion planning has yet to be agreed upon. A promising way to account for uncertainty is through the concept of risk. Risk accounts for uncertainty in assessing safety by considering both the likelihood and severity of unsafe events, such as collisions. Research has shown that human drivers navigate traffic by trading off subjectively perceived risk against the potential rewards of reaching their destinations. Additionally, studies indicate that subjective risk tends to correlate with objective risk measures, while ethicists argue that risk can be used as a means to facilitate ethicality in road traffic by distributing risk fairly among road users. These relationships highlight the benefits of incorporating risk as a proxy for generating AV behavior in motion planning algorithms, by accounting for uncertainty in unsafe events. Incorporating risk in AV motion planning presents two main challenges. The first challenge is to develop a notion of risk that correctly incorporates an AV's uncertainty about the future motion of other road users, and that can be computed fast (and precisely) enough to be used in online (i.e., real-time) motion-planning algorithms. The second challenge is to develop an appropriate AV motion planning algorithm that incorporates risk and generates the desired AV behavior, facilitating ethicality. This dissertation addresses both challenges by making six main contributions. First, we introduce a suitable risk metric: the expected severity of a collision event. This metric, thus, is constituted of two terms, one accounting for the likelihood of a collision and another term considering the severity of a collision. Second, we investigate the AV behavior given different optimization-based motion planning strategies that accommodate our risk metric. We find that a stochastic model predictive control (SMPC)-based motion planner adequately balances risk with travel efficiency in the presence of varying uncertainty. \\Third, to compute risk, we provide an computationally efficient (i.e., real-time computable) algorithm to estimate risk. Here, the focus lies on estimating the probability of collision (POC), as our risk metric reduces to a POC metric when the severity term is considered constant. Based on multi-circular shape approximations of the geometries of the AV and other road users, we present an algorithm that estimates the POC with good precision, and that is guaranteed not to under-approximate it. The algorithm is shown to execute significantly faster than standard Monte Carlo sampling, while being tailored for optimization-based motion planning strategies. In our SMPC-based motion planning strategy, it is shown to generate smooth, reproducible trajectories under varying levels of uncertainty. Fourth, we extend our POC algorithm to risk estimation. Here, we propose a severity estimation method that allows us to consider different collision constellations, as literature shows that those are associated with different levels of harm to the passengers of vehicles. The resulting risk estimation algorithm leverages the advantages of the POC estimation algorithm, i.e., its particular suitability for optimization-based motion planning strategies, while allowing for the flexible integration of severity functions to represent different collision types. Furthermore, we provide an extension of our risk metric to estimate the risk from the perspective of each road user in the driving scene, as we argue that risk is asymmetric between pairs of road users, i.e., dependent on the perspective. Fifth, given our proposed SMPC-based motion planning strategy, the AVs behavior can be tuned such that it minimizes its own risk (egoistic perspective), the risk other road users face (altruistic perspective), and a balance of both (collective perspective). To evaluate safety and driving behavior for each of these risk metrics, we performed simulations on a database containing a wide variety of real-world and artificial traffic scenarios. The results show that a collective perspective balances the overall risk of all road users with the AV's driving behavior in a favorable manner, and constitutes a suitable approach to facilitate ethicality in road traffic. Lastly, we use our proposed risk metric as part of the decision-making process on emergency maneuvers, i.e., emergency steering and emergency braking, for an AV designed to evade collisions with late-detected pedestrians. In simulations, we show that this metric outperforms deterministic risk estimation by choosing the appropriate evasive maneuver more frequently. In experimental testing, we verified the real-world applicability of this metric in an AV testbed designed for collision avoidance by having a pedestrian dummy walk out from a sight obstruction when the AV approaches. We find that risk is beneficial in deciding on accident avoidance maneuvers. The contributions introduced in this thesis show that risk, and thus uncertainty, can be systematically incorporated in AV algorithms for motion planning and decision-making. We demonstrate that our risk metric is beneficial to the avoidance capabilities of low-automation safety systems. Furthermore, the proposed algorithms are computationally efficient, allowing for real-time optimization-based motion planning, which allows highly automated vehicles to navigate smoothly through road traffic. We show that risk-based AV behavior generation enables balancing the AV's interests in safety and travel efficiency for all road users, and thus, facilitates ethicality. Y1 - 2026 UR - https://research.tue.nl/en/publications/risk-based-motion-planning-in-automated-vehicles/ UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-65845 SN - 978-90-386-6610-5 PB - Eindhoven University of Technology CY - Eindhoven ER - TY - RPRT A1 - Tolksdorf, Leon A1 - Schories, Lars A1 - Castells, Jacint A1 - Gragopoulos, Ioannis A1 - Karoui, Mouna A1 - Weihmayr, Daniel A1 - Smit, Robin A1 - Gloger, Timm A1 - Mayrargue, Sylvie A1 - Stoll, Johann A1 - Blum, Kristin A1 - Munoz Sanchez, Manuel A1 - Mannoni, Valerian A1 - Denis, Benoit T1 - D3.8 Verification Report for Demos 2, 3 and 4 Y1 - 2023 UR - https://www.safe-up.eu/deliverables ER - TY - INPR A1 - Kern, Tobias A1 - Tolksdorf, Leon A1 - Birkner, Christian T1 - Comparison of Localization Algorithms between Reduced-Scale and Real-Sized Vehicles Using Visual and Inertial Sensors UR - https://doi.org/10.48550/arXiv.2507.11241 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2507.11241 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Tolksdorf, Leon A1 - Tejada, Arturo A1 - Birkner, Christian A1 - van de Wouw, Nathan T1 - Collision Probability Estimation for Optimization-based Vehicular Motion Planning UR - https://doi.org/10.48550/arXiv.2505.21161 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2505.21161 PB - arXiv CY - Ithaca ER - TY - RPRT A1 - Löffler, Christian A1 - Gloger, Timm A1 - Silvas, Emilia A1 - Muñoz Sánchez, Manuel A1 - Tolksdorf, Leon A1 - Weihmayr, Daniel A1 - Labenski, Volker A1 - Koebe, Markus A1 - Stoll, Johann A1 - Vogl, Carina A1 - Watanabe, Hiroki A1 - Smit, Robin T1 - D3.3 Vehicle demonstrator for trajectory planning and control for combined automatic emergency braking and steering manoeuvres including system for VRU detection, motion planning and trajectory control to enhance real world performance Y1 - 2021 UR - https://www.safe-up.eu/resources ER - TY - RPRT A1 - Löffler, Christian A1 - Gloger, Timm A1 - Tolksdorf, Leon A1 - Weihmayr, Daniel A1 - Vogl, Carina A1 - Watanabe, Hiroki A1 - Stoll, Johann A1 - Labenski, Volker A1 - Koebe, Markus T1 - D3.2 Demo 2 Vehicle demonstrator for object detection in adverse weather conditions Y1 - 2021 UR - https://www.safe-up.eu/resources ER -