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Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints

Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints

Section titled “Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints”

⚠️ 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 · 轨迹优化与模型预测控制

  • topic: math-optimization
  • sources: openalex
  • retrieved_at: 2026-07-20
  • query: trajectory optimization autonomous vehicles
  • doi: 10.1109/tvt.2016.2555853
  • score_total: 58
  • suggested_tier: foundational

(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 and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints.

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

围绕「Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints」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 and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints)
  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 and Tracking for Vehicle Collision Avoidance Based on Model Predic

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 2016
Authors Jie Ji, Amir Khajepour, Wael William Melek, Yanjun Huang
arXiv
DOI 10.1109/tvt.2016.2555853
Topics motion-planning-control, math-optimization
展开 Extract / Selections / Local assets
  • motion-planning-control: tier=foundational rank=2 score=59 — auto refresh 2026-07-19 sources=openalex
  • math-optimization: tier=foundational rank=1 score=58 — TVT 2016 path planning/tracking with MPC for collision avoidance
(no PDF text available; metadata-only card)