Time-Optimal Nonlinear Model Predictive Control for Radar-based Automated Parking. Issue 14 (2022)

• Record Type: Journal Article
• Authors: Diehl, Christopher; Makarow, Artemi; Rösmann, Christoph; Bertram, Torsten
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 14(2022)
• Page Start: 34
• Page End: 39
• Publication Date: 2022
• Subjects: automated driving -- predictive control -- trajectory planning -- obstacle avoidance -- perception -- radar; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.07.579
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Motion planning and control are crucial components for automated vehicles. Especially in parking scenarios, high precision is required due to the small distances to obstacles. Common approaches utilize ultrasonic or camera sensors. This paper presents a radar-based system architecture for automated parking, which can operate more robustly under difficult weather conditions. Moreover, this work develops a unified planning and control framework based on nonlinear model predictive control. Real vehicles often exhibit dynamic behavior that the commonly used kinematic bicycle model does not represent. Therefore, the paper at hand proposes an extended model, which rests upon a systematic analysis of the test vehicle's dynamics for the low-velocity range. Experiments in simulation and on real-world data show the efficiency of the approach and the importance of the extended vehicle dynamics modeling for closed-loop control.