2023
DOI: 10.1007/s12555-022-0183-8
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Path Tracking and Local Obstacle Avoidance for Automated Vehicle Based on Improved Artificial Potential Field

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Cited by 2 publications
(1 citation statement)
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“…In the study by Li et al [25], they utilize this approach to extend its utilization to the design of time headway and lane-changing functions, yielding tailored safety trajectory planning across diverse driving styles. Furthermore, in the research conducted by Li and colleagues [26], they enhance the artificial potential field paradigm to address cooperative control challenges pertinent to local obstacle avoidance and path tracking in autonomous vehicles. Collectively, these advancements underscore the substantial potential of the artificial potential field method within the realm of intelligent transportation systems.…”
Section: Introductionmentioning
confidence: 99%
“…In the study by Li et al [25], they utilize this approach to extend its utilization to the design of time headway and lane-changing functions, yielding tailored safety trajectory planning across diverse driving styles. Furthermore, in the research conducted by Li and colleagues [26], they enhance the artificial potential field paradigm to address cooperative control challenges pertinent to local obstacle avoidance and path tracking in autonomous vehicles. Collectively, these advancements underscore the substantial potential of the artificial potential field method within the realm of intelligent transportation systems.…”
Section: Introductionmentioning
confidence: 99%