Conference
Trajectory Modelling for Autonomous Driving: Investigating the Artificial Potential Field Method
| العنوان: | Trajectory Modelling for Autonomous Driving: Investigating the Artificial Potential Field Method |
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| المؤلفون: | Zieglmeir, Andreas, Hirose, Toshiya, Schneider, Stefan-Alexander |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | ddc:60 |
| الوصف: | 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. |
| نوع الوثيقة: | conference object |
| وصف الملف: | application/pdf |
| اللغة: | English |
| DOI: | 10.60785/opus-2161 |
| الاتاحة: | https://opus4.kobv.de/opus4-hs-kempten/frontdoor/index/index/docId/2161 https://nbn-resolving.org/urn:nbn:de:bvb:859-21611 https://doi.org/10.60785/opus-2161 https://opus4.kobv.de/opus4-hs-kempten/files/2161/A3-4_021_Andreas_Zieglmeir.pdf |
| Rights: | https://creativecommons.org/licenses/by/4.0/deed.de ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.2AD7C4C8 |
| قاعدة البيانات: | BASE |
| DOI: | 10.60785/opus-2161 |
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