A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
Section titled “A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles”⚠️ 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 运动规划与控制
Discovery evidence
Section titled “Discovery evidence”- topic:
motion-planning-control - sources:
openalex - retrieved_at: 2026-07-20
- query: motion planning control autonomous vehicles
- doi:
10.1109/iv48863.2021.9575880 - score_total: 51
- suggested_tier:
recent
Relevance
Section titled “Relevance”(no prose relevance explanation — numeric score only or HTTP source)
Snippets
Section titled “Snippets”(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
One-line takeaway
Section titled “One-line takeaway”Metadata-only card for A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles.
Problem & motivation
Section titled “Problem & motivation”—
Inputs & outputs
Section titled “Inputs & outputs”—
Core representation
Section titled “Core representation”—
Method pipeline & key modules
Section titled “Method pipeline & key modules”—
Training objectives
Section titled “Training objectives”—
Datasets & metrics
Section titled “Datasets & metrics”—
Main results
Section titled “Main results”—
Ablation findings
Section titled “Ablation findings”—
Limitations & failure modes
Section titled “Limitations & failure modes”—
Related work positioning
Section titled “Related work positioning”—
Open-source / code anchors
Section titled “Open-source / code anchors”—
Reproduction risks
Section titled “Reproduction risks”—
Practical value
Section titled “Practical value”—
Evidence
Section titled “Evidence”—
延伸解读与背景补充
Section titled “延伸解读与背景补充”生成：2026-07-21 · 来源条数 0 · 模型
heuristic· 需人工核验数字
围绕「A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles」的核心问题与动机（待结合全文核验）。
方法要点补强
Section titled “方法要点补强”- 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
Section titled “与相近工作的关系”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。
- 这篇工作的输入/输出表示是什么？（A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles）
- 训练目标与评测协议各是什么？
- 主要失败模式或局限是什么？
外部解读索引
Section titled “外部解读索引”- （本次未抓取到白名单二次解读页）
方法结构（重绘）
Section titled “方法结构（重绘）”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: A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Contr方法结构示意（重绘；细节以原论文为准，待 PDF 核验）。
英文自动分析（可折叠）
Section titled “英文自动分析（可折叠）”展开英文 Paper Card / AI deep analysis
Paper Card
Section titled “Paper Card”| Field | Content |
|---|---|
| Year | 2021 |
| Authors | Fei Ye, Shen Zhang, Pin Wang, Ching‐Yao Chan |
| arXiv | — |
| DOI | 10.1109/iv48863.2021.9575880 |
| Topics | motion-planning-control |
原文摘录与素材（可折叠）
Section titled “原文摘录与素材（可折叠）”展开 Extract / Selections / Local assets
Selections
Section titled “Selections”motion-planning-control: tier=needs-review rank=5 score=51 — auto refresh 2026-07-19 sources=openalex
Extract excerpt
Section titled “Extract excerpt”(no PDF text available; metadata-only card)