2022
DOI: 10.1109/tits.2022.3177608
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3D Gradient Reconstruction-Based Path Planning Method for Autonomous Vehicle With Enhanced Roll Stability

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Cited by 12 publications
(4 citation statements)
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“…Thus, the planning process necessitates adaptive weight adjustments to accommodate various off‐road environments. While Liu et al (2022) proposes using fuzzy reasoning to adaptively modify the weights of roll comfort and potential fields, this approach relies primarily on rule‐based lookup using empirical rules, which often have limited applicability to specific scenarios. In contrast, the proposed method in this paper offers an adaptive weighst allocation mechanism for multiple costs, based on human driving experience, enabling effective adaptation to different scenarios.…”
Section: Related Workmentioning
confidence: 99%
“…Thus, the planning process necessitates adaptive weight adjustments to accommodate various off‐road environments. While Liu et al (2022) proposes using fuzzy reasoning to adaptively modify the weights of roll comfort and potential fields, this approach relies primarily on rule‐based lookup using empirical rules, which often have limited applicability to specific scenarios. In contrast, the proposed method in this paper offers an adaptive weighst allocation mechanism for multiple costs, based on human driving experience, enabling effective adaptation to different scenarios.…”
Section: Related Workmentioning
confidence: 99%
“…Since the road conditions at each wheel of the vehicle do not differ much during driving process, the road model at the four wheels is the same as that at the front w 1. According to the harmonic superposition method, references [22,23], a B class 3D model with a length of 400 m and a width of 4 m is established as shown in Figure 3. Since the road conditions at each wheel of the vehicle do not differ much during the driving process, the road model at the four wheels is the same as that at the front wheel 1.…”
Section: Pavement Simulation Modelmentioning
confidence: 99%
“…Path planning presents difficulties due to its non-linear, multi-constraint nature within uncertain environments, making optimal solutions elusive using traditional techniques (Sanchez-Ibanez et al, 2021 ). Conventional algorithms like the A * (Jin et al, 2022 ), probabilistic roadmaps (Kavraki et al, 1996 ), fuzzy reasoning (Liu Z. X. et al, 2022 ), artificial potential field (Das et al, 2022 ), and rapidly-exploring random trees (Zhang et al, 2021 ) have limitations addressing complex environments. Swarm intelligence algorithms excel through self-organizing population interactions informed by mathematical rulesets (Xu et al, 2020 ).…”
Section: Introductionmentioning
confidence: 99%