TY - JOUR A1 - Coll-Perales, Baldomero A1 - Schulte-Tigges, Joschua A1 - Rondinone, Michele A1 - Gozalvez, Javier A1 - Reke, Michael A1 - Matheis, Dominik A1 - Walter, Thomas T1 - Prototyping and evaluation of infrastructure-assisted transition of control for cooperative automated vehicles JF - IEEE Transactions on Intelligent Transportation Systems N2 - Automated driving is now possible in diverse road and traffic conditions. However, there are still situations that automated vehicles cannot handle safely and efficiently. In this case, a Transition of Control (ToC) is necessary so that the driver takes control of the driving. Executing a ToC requires the driver to get full situation awareness of the driving environment. If the driver fails to get back the control in a limited time, a Minimum Risk Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g., decelerating to full stop). The execution of ToCs requires some time and can cause traffic disruption and safety risks that increase if several vehicles execute ToCs/MRMs at similar times and in the same area. This study proposes to use novel C-ITS traffic management measures where the infrastructure exploits V2X communications to assist Connected and Automated Vehicles (CAVs) in the execution of ToCs. The infrastructure can suggest a spatial distribution of ToCs, and inform vehicles of the locations where they could execute a safe stop in case of MRM. This paper reports the first field operational tests that validate the feasibility and quantify the benefits of the proposed infrastructure-assisted ToC and MRM management. The paper also presents the CAV and roadside infrastructure prototypes implemented and used in the trials. The conducted field trials demonstrate that infrastructure-assisted traffic management solutions can reduce safety risks and traffic disruptions. KW - Automated driving KW - automated vehicles KW - connected automated vehicles KW - CAV KW - experimental evaluation Y1 - 2021 U6 - http://dx.doi.org/10.1109/TITS.2021.3061085 SN - 1524-9050 (Print) SN - 1558-0016 (Online) VL - 23 IS - 7 SP - 6720 EP - 6736 PB - IEEE ER - TY - CHAP A1 - Schulte-Tigges, Joschua A1 - Matheis, Dominik A1 - Reke, Michael A1 - Walter, Thomas A1 - Kaszner, Daniel ED - Krömker, Heidi T1 - Demonstrating a V2X enabled system for transition of control and minimum risk manoeuvre when leaving the operational design domain T2 - HCII 2023: HCI in Mobility, Transport, and Automotive Systems N2 - Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle’s software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany. KW - V2X KW - Transiton of Control KW - Minimum Risk Manoeuvre KW - Operational Design Domain KW - Connected Automated Vehicle Y1 - 2023 SN - 978-3-031-35677-3 (Print) SN - 978-3-031-35678-0 (Online) U6 - http://dx.doi.org/10.1007/978-3-031-35678-0_12 N1 - 5th International Conference, MobiTAS 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023. SP - 200 EP - 210 PB - Springer CY - Cham ER - TY - JOUR A1 - Schulte-Tigges, Joschua A1 - Förster, Marco A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Ferrein, Alexander A1 - Kaszner, Daniel A1 - Matheis, Dominik A1 - Walter, Thomas T1 - Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments JF - Sensors N2 - Abstract In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars. KW - Lidar KW - Benchmark KW - Self-driving Y1 - 2022 U6 - http://dx.doi.org/10.3390/s22197146 SN - 1424-8220 N1 - This article belongs to the Special Issue "Sensor Fusion for Vehicles Navigation and Robotic Systems" VL - 22 IS - 19 PB - MDPI CY - Basel ER - TY - CHAP A1 - Chajan, Eduard A1 - Schulte-Tigges, Joschua A1 - Reke, Michael A1 - Ferrein, Alexander A1 - Matheis, Dominik A1 - Walter, Thomas T1 - GPU based model-predictive path control for self-driving vehicles T2 - IEEE Intelligent Vehicles Symposium (IV) N2 - One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments. KW - Heuristic algorithms KW - Computational modeling KW - model-predictive control KW - GPU KW - autonomous driving Y1 - 2021 SN - 978-1-7281-5394-0 U6 - http://dx.doi.org/10.1109/IV48863.2021.9575619 N1 - 2021 IEEE Intelligent Vehicles Symposium (IV) July 11-17, 2021. Nagoya, Japan SP - 1243 EP - 1248 PB - IEEE ER - TY - CHAP A1 - Reke, Michael A1 - Peter, Daniel A1 - Schulte-Tigges, Joschua A1 - Schiffer, Stefan A1 - Ferrein, Alexander A1 - Walter, Thomas A1 - Matheis, Dominik T1 - A Self-Driving Car Architecture in ROS2 T2 - 2020 International SAUPEC/RobMech/PRASA Conference, Cape Town, South Africa Y1 - 2020 SN - 978-1-7281-4162-6 U6 - http://dx.doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041020 SP - 1 EP - 6 ER -