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  - 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  - 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  -