TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks

arXiv:2609.39969 · cs.RO, cs.AI, cs.SY, eess.SY · Submitted 2026-09-30 · Read on arXiv

cs.RO, cs.AI, cs.SY, eess.SY

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/gdorothy/TACTIC-Roadside-LiDAR-Attack

Terminology

• Ablation — 7× in this paper · explained in 1 episode
• Physical Constraints — 5× in this paper · explained in 7 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Action Space — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Computer Vision and Pattern Recognition — 2× in this paper
• Failure Modes — 2× in this paper · explained in 3 episodes
• IEEE Symposium on Security and Privacy — 2× in this paper
• Point Clouds — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• SoK: How Sensor Attacks Disrupt Autonomous Vehicles: An End-to-end Analysis, Challenges, and Missed Threats
• LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models
• GPT-Driver: Learning to Drive with GPT
• SEIDM: A Safe and Efficient Intelligent Driver Model for Autonomous Driving Behavior
• Qwen3-VL Technical Report
• Kimi K2: Open Agentic Intelligence

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