D4+: Emergent Adversarial Driving Maneuvers with Approximate Functional Optimization

• URL: http://arxiv.org/abs/2505.13942v1
• Date: Tue, 20 May 2025 05:22:03 GMT
• Title: D4+: Emergent Adversarial Driving Maneuvers with Approximate Functional Optimization
• Authors: Diego Ortiz Barbosa, Luis Burbano, Carlos Hernandez, Zengxiang Lei, Younghee Park, Satish Ukkusuri, Alvaro A Cardenas,
• Abstract summary: We implement a scenario-based framework with a formal method to identify the impact of malicious drivers interacting with autonomous vehicles.<n>Our results can help designers identify the range of safe operational behaviors that prevent malicious drivers from exploiting the autonomous features of modern vehicles.
• Score: 3.763470738887407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intelligent mechanisms implemented in autonomous vehicles, such as proactive driving assist and collision alerts, reduce traffic accidents. However, verifying their correct functionality is difficult due to complex interactions with the environment. This problem is exacerbated in adversarial environments, where an attacker can control the environment surrounding autonomous vehicles to exploit vulnerabilities. To preemptively identify vulnerabilities in these systems, in this paper, we implement a scenario-based framework with a formal method to identify the impact of malicious drivers interacting with autonomous vehicles. The formalization of the evaluation requirements utilizes metric temporal logic (MTL) to identify a safety condition that we want to test. Our goal is to find, through a rigorous testing approach, any trace that violates this MTL safety specification. Our results can help designers identify the range of safe operational behaviors that prevent malicious drivers from exploiting the autonomous features of modern vehicles.

Related papers

• SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator [77.86600052899156]
  Large Language Model (LLM)-based agents are increasingly deployed in real-world applications.<n>We propose AutoSafe, the first framework that systematically enhances agent safety through fully automated synthetic data generation.<n>We show that AutoSafe boosts safety scores by 45% on average and achieves a 28.91% improvement on real-world tasks.
  arXiv  Detail & Related papers  (2025-05-23T10:56:06Z)
• AttentionGuard: Transformer-based Misbehavior Detection for Secure Vehicular Platoons [0.0]
  Vehicle platooning is vulnerable to sophisticated falsification attacks by authenticated insiders.<n>We present AttentionGuard, a transformer-based framework for misbehavior detection.<n>We show that AttentionGuard achieves up to 0.95 F1-score in attack detection, with robust performance maintained during complex maneuvers.
  arXiv  Detail & Related papers  (2025-05-15T13:24:09Z)
• Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
  We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
  arXiv  Detail & Related papers  (2025-04-30T13:47:25Z)
• SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models [63.71984266104757]
  Multimodal Large Language Models (MLLMs) can process both visual and textual data.<n>We propose SafeAuto, a novel framework that enhances MLLM-based autonomous driving systems by incorporating both unstructured and structured knowledge.
  arXiv  Detail & Related papers  (2025-02-28T21:53:47Z)
• CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening [16.305837225117607]
  This paper introduces CRASH - Challenging Reinforcement-learning based Adversarial scenarios for Safety Hardening. First CRASH can control adversarial Non Player Character (NPC) agents in an AV simulator to automatically induce collisions with the Ego vehicle. We also propose a novel approach, that we term safety hardening, which iteratively refines the motion planner by simulating improvement scenarios against adversarial agents.
  arXiv  Detail & Related papers  (2024-11-26T00:00:27Z)
• Enhancing Road Safety: Real-Time Detection of Driver Distraction through Convolutional Neural Networks [0.0]
  This study seeks to identify the most efficient model for real-time detection of driver distractions. The ultimate aim is to incorporate the findings into vehicle safety systems, significantly boosting their capability to prevent accidents triggered by inattention.
  arXiv  Detail & Related papers  (2024-05-28T03:34:55Z)
• Physical Backdoor Attack can Jeopardize Driving with Vision-Large-Language Models [53.701148276912406]
  Vision-Large-Language-models (VLMs) have great application prospects in autonomous driving. BadVLMDriver is the first backdoor attack against VLMs for autonomous driving that can be launched in practice using physical objects. BadVLMDriver achieves a 92% attack success rate in inducing a sudden acceleration when coming across a pedestrian holding a red balloon.
  arXiv  Detail & Related papers  (2024-04-19T14:40:38Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
  This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems. LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
  arXiv  Detail & Related papers  (2023-11-28T03:13:09Z)
• Adaptive Failure Search Using Critical States from Domain Experts [9.93890332477992]
  Failure search may be done through logging substantial vehicle miles in either simulation or real world testing. AST is one such method that poses the problem of failure search as a Markov decision process. We show that the incorporation of critical states into the AST framework generates failure scenarios with increased safety violations.
  arXiv  Detail & Related papers  (2023-04-01T18:14:41Z)
• Attacks and Faults Injection in Self-Driving Agents on the Carla Simulator -- Experience Report [1.933681537640272]
  We report on the injection of adversarial attacks and software faults in a self-driving agent running in a driving simulator. We show that adversarial attacks and faults injected in the trained agent can lead to erroneous decisions and severely jeopardize safety.
  arXiv  Detail & Related papers  (2022-02-25T21:46:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.