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Trajectory Modelling for Autonomous Driving: Investigating the Artificial Potential Field Method

  • Although the focus of autonomous driving is on maximizing safety and efficiency, comfort and familiarity will play a key role in the adoption of autonomous driving. Therefore, it is important to develop algorithms that can mimic human driving skills and adapt to individual driving styles. The potential field method (PFM) is an obstacle avoidance algorithm for autonomous driving that uses a repulsive potential field, as a environment model, to navigate the vehicle to the lowest risk potential. In this paper, the PFM is used in a overtake scenario at high speed, to test the impact of using prediction when calculating the ideal yaw rate. Analysis is done on how the potential field can be used for lane keeping while following a car and then for overtaking it. A driving simulator is used to record human driving data and compare it with automated driving using a PFM as is proposed by [3], with modifications to enable future prediction.

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Metadaten
Author:Andreas Zieglmeir, Toshiya Hirose, Stefan-Alexander SchneiderORCiDGND
URN:urn:nbn:de:bvb:859-21611
DOI:https://doi.org/10.60785/opus-2161
Place of publication:Kempten
Document Type:conference proceeding (article)
Conference:2023-2024 JSAE KANTO International Conference of Automotive Technology for Young Engineers (ICATYE)
Language:English
Year of first Publication:2024
Publishing Institution:Hochschule für angewandte Wissenschaften Kempten
Tag:autonomous driving; driver model; driving simulator; hazard map; lateral control; potential field; repulsive field; risk curve; safety
Number of pages:3 Seiten
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 60 Technik
Open Access:open_access
Research focus:FSP2: Mobilität
Publication Lists:Schneider, Stefan-Alexander
Publication reviewed:begutachtet
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2024/04/30
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