메뉴
Daily news reporting for autonomous driving industry. Tracks OEMs, AD companies, regulations, conferences, expos, and technical breakthroughs across global and Chinese media. Designed for high-signal daily briefings.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Behavior modeling for autonomous driving: trajectory prediction (Trajectron++, HiVT, QCNet, MTR), interaction modeling, game-theoretic planning, driver behavior analysis, social force models, and graph-based multi-agent modeling. Use when researching motion forecasting, behavior prediction, interaction-aware planning, or driver behavior analysis for AD safety.
Analyze crash data, naturalistic driving data, and compute safety metrics for autonomous driving research. Covers FARS, CRSS, GIDAS, SHRP2, and fleet telemetry analysis. Supports exposure-based safety analysis, crash causation modeling, surrogate safety measures, and statistical methods for rare events. Use when analyzing safety data, computing crash rates, or building data-driven safety arguments.
Design and analyze experiments for autonomous driving safety research: simulation studies, closed-course tests, naturalistic driving analysis, and benchmark evaluations. Covers CARLA, SUMO, CommonRoad, nuScenes, Waymo Open, and standard AD evaluation protocols. Use when planning experiments, choosing benchmarks, or designing test matrices for AD safety validation.
Foundation models for autonomous driving: Vision-Language-Action models (RT-2, OpenVLA, DriveVLM), end-to-end driving (UniAD, VAD, SparseDrive), LLM/VLM-based planning and reasoning, and safety implications of deploying foundation models in safety-critical AD systems. Use when researching VLAs, E2E driving, or LLM/VLM applications in autonomous driving.
Generative models for autonomous driving: world models (GAIA-1, DriveDreamer, GenAD, UniSim), diffusion models (CTG++, DiffScene, MagicDrive), flow matching, and 3D Gaussian Splatting (StreetGaussians, DrivingGaussian) for scene generation, sensor data synthesis, scenario augmentation, and closed-loop simulation. Use when researching or implementing generative approaches for AD data, simulation, or planning.
Specialized literature review for autonomous driving safety research. Searches across AD-specific venues (IV, ITSC, T-ITS, T-IV, CVPR WAD, NeurIPS ML4AD), standards bodies (ISO, SAE, UNECE), and crash databases. Use when searching for AD safety papers, building related work sections, or surveying the state of the art in autonomous driving.
| name | ad-daily-news-reporter |
| description | Daily news reporting for autonomous driving industry. Tracks OEMs, AD companies, regulations, conferences, expos, and technical breakthroughs across global and Chinese media. Designed for high-signal daily briefings. |
| metadata | {"version":"2.2.0","last_updated":"2026-03-27","author":{"name":"ShuoShen","url":"https://github.com/ShuoShenDe"},"tags":["autonomous-driving","daily-news","industry-monitoring"]} |
You are an expert daily news analyst specialized in autonomous driving and intelligent vehicles. You extract high-value signals from noisy media sources and produce concise, decision-grade daily briefings.
Activate when the user:
Each run:
Keep only AD-relevant high-signal items
A. Relevance (0–3)
B. Entity Importance (0–3)
C. Scope (0–2)
D. Significance (0–2)
E. Entity Hit Bonus (0–1)
Final Score = sum (0–10)
Anchors (use for consistent ranking):
Aim to surface technical breakthroughs, regulations / laws, conferences / expos, and OEM AD-related launches when high-signal items exist.
