The OPA<sup>3</sup>L System and Testconcept for Urban Autonomous Driving

Folkers, Andreas; Wellhausen, Constantin; Rick, Matthias; Li, Xibo; Evers, Lennart; Schwarting, Verena; Clemens, Joachim; Dittmann, Philipp; Shubbak, Mahmood; Bustert, Tom; Zachmann, Gabriel; Schill, Kerstin; Büskens, Christof

doi:10.1109/itsc55140.2022.9922416

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…We chose the constants to be l := 2.786 m, δ max := 0.55 rad, a min := −4.5 m s 2 , δ vmax := 1.2 rad s , j max := 20 m s 3 (24) based on the research vehicle described in [36]. Thereby, l describes the wheelbase, and the other constants model the physical limitations of the car in normal driving mode.…”

Section: Inputsmentioning

confidence: 99%

Computing Safe Stop Trajectories for Autonomous Driving Utilizing Clustering and Parametric Optimization

Langhorst,

Chan,

Meerpohl

et al. 2024

Vehicles

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In the realm of autonomous driving, ensuring a secure halt is imperative across diverse scenarios, ranging from routine stops at traffic lights to critical situations involving detected system boundaries of crucial modules. This article presents a novel methodology for swiftly calculating safe stop trajectories. We utilize a clustering method to categorize lane shapes to assign encountered traffic situations at runtime to a set of precomputed resources. Among these resources, there are precalculated halt trajectories along representative lane centers that serve as parametrizations of the optimal control problem. At runtime, the current road settings are identified, and the respective precomputed trajectory is selected and then adjusted to fit the present situation. Here, the perceived lane center is considered a change in the parameters of the optimal control problem. Thus, techniques based on parametric sensitivity analysis can be employed, such as the low-cost feasibility correction. This approach covers a substantial number of lane shapes and exhibits a similar solution quality as a re-optimization to generate a trajectory while demanding only a fraction of the computation time.

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Section: Inputsmentioning

confidence: 99%

Computing Safe Stop Trajectories for Autonomous Driving Utilizing Clustering and Parametric Optimization

Langhorst,

Chan,

Meerpohl

et al. 2024

Vehicles

View full text Add to dashboard Cite

show abstract

Cascaded Disturbance Compensation for MPC-based Autonomous Vehicle Guidance

Jalilian

Schwarz

Völz

et al. 2023

2023 31st Mediterranean Conference on Control and Automation (MED)

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PRORETA 5 – building blocks for automated urban driving enhancing city road safety

Popp,

Serov,

Glatzki

et al. 2024

At - Automatisierungstechnik

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In the joint research project PRORETA 5, building blocks for automated driving in urban areas have been developed, implemented, and tested. The developed blocks involve an object tracking for cars, bicycles, and pedestrians that feeds a multimodal object prediction which is able to predict the traffic participants’ most likely trajectories. Then, an anytime tree-based planning algorithm calculates the vehicle’s desired path. Finally, logic-based safety functions ensure a collision-free trajectory for the ego vehicle. The mentioned building blocks were integrated and tested in a prototype vehicle in urban scenarios. Furthermore, a novel general framework for specifying and testing traffic rule compliance has been developed. In this paper, the automated driving concept of PRORETA 5 is introduced and the developed methods are briefly explained.

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The OPA³L System and Testconcept for Urban Autonomous Driving

Cited by 4 publications

References 19 publications

Computing Safe Stop Trajectories for Autonomous Driving Utilizing Clustering and Parametric Optimization

Computing Safe Stop Trajectories for Autonomous Driving Utilizing Clustering and Parametric Optimization

Cascaded Disturbance Compensation for MPC-based Autonomous Vehicle Guidance

PRORETA 5 – building blocks for automated urban driving enhancing city road safety

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The OPA3L System and Testconcept for Urban Autonomous Driving

Cited by 4 publications

References 19 publications

Computing Safe Stop Trajectories for Autonomous Driving Utilizing Clustering and Parametric Optimization

Computing Safe Stop Trajectories for Autonomous Driving Utilizing Clustering and Parametric Optimization

Cascaded Disturbance Compensation for MPC-based Autonomous Vehicle Guidance

PRORETA 5 – building blocks for automated urban driving enhancing city road safety

Contact Info

Product

Resources

About

The OPA³L System and Testconcept for Urban Autonomous Driving