跳转到内容

Path planning algorithms in the autonomous driving system: A comprehensive review

Path planning algorithms in the autonomous driving system: A comprehensive review

Section titled “Path planning algorithms in the autonomous driving system: A comprehensive review”

⚠️ AI 生成 · 建议对照原文 本页为自动整理的学习笔记;关键数据与引用如需引用,请回查 PDF / 官方版本。

学习档位 自动卡

类型 文献 · 更新 2026-07-19

所属 自动驾驶 3D 感知、时序融合与跟踪 · 基准、评测与安全 · 扩散模型 · 运动规划与控制

  • topic: ad-end-to-end-driving
  • sources: openalex
  • retrieved_at: 2026-07-20
  • query: end-to-end autonomous driving planning perception
  • doi: 10.1016/j.robot.2024.104630
  • score_total: 55
  • suggested_tier: recent

(no prose relevance explanation — numeric score only or HTTP source)

(no snippet evidence in candidate pool)

AI analysis (heuristic fallback, needs-source-verification)

Section titled “AI analysis (heuristic fallback, needs-source-verification)”

Status: metadata_only · Source: metadata

Metadata-only card for Path planning algorithms in the autonomous driving system: A comprehensive review.

生成:2026-07-21 · 来源条数 0 · 模型 heuristic · 需人工核验数字

围绕「Path planning algorithms in the autonomous driving system: A comprehensive review」的核心问题与动机(待结合全文核验)。

  • 见原文方法章节;以下为基于摘要/摘录的要点提示。
  • 本地摘录暂缺。

与相近工作的关系待核验;请对照 related work。

  • 勿仅凭摘要推断未给出的数值指标。
  1. 这篇工作的输入/输出表示是什么?(Path planning algorithms in the autonomous driving system: A comprehensive review)
  2. 训练目标与评测协议各是什么?
  3. 主要失败模式或局限是什么?
  • (本次未抓取到白名单二次解读页)
flowchart LR
A["输入 / 观测"] --> B["表示 / 编码"]
B --> C["推理 / 解码"]
C --> D["输出 / 动作或检测"]
%% method sketch for: Path planning algorithms in the autonomous driving system: A comprehensive revie

方法结构示意(重绘;细节以原论文为准,待 PDF 核验)。

展开英文 Paper Card / AI deep analysis
Field Content
Year 2024
Authors M. Reda, Ahmed Onsy, A. Haikal, Ali Ghanbari
arXiv 2024.10463
DOI 10.1016/j.robot.2024.104630
Topics ad-perception-tracking, benchmark-eval-safety, diffusion-models, motion-planning-control, prediction-planning-control
Paper https://arxiv.org/abs/2024.10463
展开 Extract / Selections / Local assets
  • ad-perception-tracking: tier=needs-review rank=8 score=50 — auto refresh 2026-07-19 sources=openalex
  • autonomous-driving: tier=recent rank=2 score=67 — auto refresh 2026-07-19 sources=openalex
  • benchmark-eval-safety: tier=recent rank=1 score=52 — auto refresh 2026-07-19 sources=openalex | promoted watch->recent for coverage fill
  • diffusion-models: tier=recent rank=2 score=52 — auto refresh 2026-07-19 sources=openalex | promoted watch->recent for coverage fill
  • motion-planning-control: tier=recent rank=5 score=55 — auto refresh 2026-07-19 sources=openalex
  • prediction-planning-control: tier=watch rank=4 score=55 — auto refresh 2026-07-19 sources=openalex
(no PDF text available; metadata-only card)