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End-to-End Autonomous Driving: Challenges and Frontiers

End-to-End Autonomous Driving: Challenges and Frontiers

Section titled “End-to-End Autonomous Driving: Challenges and Frontiers”

⚠️ 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 端到端驾驶 · 端到端学习 · 预测、规划与控制

  • topic: prediction-planning-control
  • sources: asta, openalex
  • retrieved_at: 2026-07-20
  • query: Find foundational and recent research papers for the topic «预测规划控制(通用)» (prediction-planning-control). Prefer peer-reviewed or widely cited work with clear method contributions. Include open-source code when available. Exclude pure survey spam unless highly cited. Core concepts: joint prediction planning, integrated prediction planning, coupled prediction. Search facets: joint motion prediction and planning autonomous driving; integrated prediction planning control vehicles; coupled prediction a
  • corpus_id: 259287283
  • doi: 10.1109/tpami.2024.3435937
  • relevance_score: 0.5304661465644493
  • score_total: 69
  • 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 End-to-End Autonomous Driving: Challenges and Frontiers.

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

围绕「End-to-End Autonomous Driving: Challenges and Frontiers」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?(End-to-End Autonomous Driving: Challenges and Frontiers)
  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: End-to-End Autonomous Driving: Challenges and Frontiers

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 Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, Hongyang Li
arXiv
DOI 10.1109/tpami.2024.3435937
Topics ad-end-to-end-driving, end-to-end-learning, prediction-planning-control
展开 Extract / Selections / Local assets
  • ad-end-to-end-driving: tier=foundational rank=2 score=69 — auto refresh 2026-07-19 sources=openalex | promoted watch->foundational for coverage fill
  • autonomous-driving: tier=recent rank=1 score=69 — TPAMI E2E AD challenges and frontiers survey
  • end-to-end-learning: tier=recent rank=2 score=60 — TPAMI 2024 End-to-End Autonomous Driving: Challenges and Frontiers — authoritative survey
  • prediction-planning-control: tier=recent rank=1 score=66 — auto refresh 2026-07-19 sources=openalex
(no PDF text available; metadata-only card)