CATS: A framework for Cooperative Autonomy Trust & Security
- URL: http://arxiv.org/abs/2503.00659v1
- Date: Sat, 01 Mar 2025 23:18:40 GMT
- Title: CATS: A framework for Cooperative Autonomy Trust & Security
- Authors: Namo Asavisanu, Tina Khezresmaeilzadeh, Rohan Sequeira, Hang Qiu, Fawad Ahmad, Konstantinos Psounis, Ramesh Govindan,
- Abstract summary: We introduce CATS, an automated system that blends together the best traits of reputation-based and majority-based detection mechanisms.<n>Our evaluation with city-scale simulations on realistic traffic data shows CATS's effectiveness in rapidly identifying and isolating misbehaving vehicles.
- Score: 8.093531393348469
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With cooperative perception, autonomous vehicles can wirelessly share sensor data and representations to overcome sensor occlusions, improving situational awareness. Securing such data exchanges is crucial for connected autonomous vehicles. Existing, automated reputation-based approaches often suffer from a delay between detection and exclusion of misbehaving vehicles, while majority-based approaches have communication overheads that limits scalability. In this paper, we introduce CATS, a novel automated system that blends together the best traits of reputation-based and majority-based detection mechanisms to secure vehicle-to-everything (V2X) communications for cooperative perception, while preserving the privacy of cooperating vehicles. Our evaluation with city-scale simulations on realistic traffic data shows CATS's effectiveness in rapidly identifying and isolating misbehaving vehicles, with a low false negative rate and overheads, proving its suitability for real world deployments.
Related papers
- Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication [4.575903181579272]
We propose a cooperative-perception-based anomaly detection framework (CPAD)<n>CPAD is a robust architecture that remains effective under communication interruptions.<n> Empirical results demonstrate that our approach outperforms standard anomaly classification methods in F1-score, AUC.
arXiv Detail & Related papers (2025-01-28T22:41:06Z) - Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework [79.088116316919]
Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory.
This paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework.
arXiv Detail & Related papers (2024-09-19T14:36:00Z) - Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework.
To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies.
We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
arXiv Detail & Related papers (2024-08-29T08:53:26Z) - SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and
Judger Mechanism [23.824400533836535]
We introduce SmartCooper, an adaptive collaborative perception framework that incorporates communication optimization and a judger mechanism.
Our results demonstrate a substantial reduction in communication costs by 23.10% compared to the non-judger scheme.
arXiv Detail & Related papers (2024-02-01T04:15:39Z) - NLOS Dies Twice: Challenges and Solutions of V2X for Cooperative
Perception [7.819255257787961]
We introduce an abstract perception matrix matching method for quick sensor fusion matching procedures and mobility-height hybrid relay determination procedures.
To demonstrate the effectiveness of our solution, we design a new simulation framework to consider autonomous driving, sensor fusion and V2X communication in general.
arXiv Detail & Related papers (2023-07-13T08:33:02Z) - Convergence of Communications, Control, and Machine Learning for Secure
and Autonomous Vehicle Navigation [78.60496411542549]
Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations.
This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
arXiv Detail & Related papers (2023-07-05T21:38:36Z) - Selective Communication for Cooperative Perception in End-to-End
Autonomous Driving [8.680676599607123]
We propose a novel selective communication algorithm for cooperative perception.
Our algorithm is shown to produce higher success rates than a random selection approach on previously studied safety-critical driving scenario simulations.
arXiv Detail & Related papers (2023-05-26T18:13:17Z) - Reinforcement Learning based Cyberattack Model for Adaptive Traffic
Signal Controller in Connected Transportation Systems [61.39400591328625]
In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time.
This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes.
One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network.
An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
arXiv Detail & Related papers (2022-10-31T20:12:17Z) - COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked
Vehicles [54.61668577827041]
We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving.
Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
arXiv Detail & Related papers (2022-05-04T17:55:12Z) - Learning to Communicate and Correct Pose Errors [75.03747122616605]
We study the setting proposed in V2VNet, where nearby self-driving vehicles jointly perform object detection and motion forecasting in a cooperative manner.
We propose a novel neural reasoning framework that learns to communicate, to estimate potential errors, and to reach a consensus about those errors.
arXiv Detail & Related papers (2020-11-10T18:19:40Z) - Cooperative Perception with Deep Reinforcement Learning for Connected
Vehicles [7.7003495898919265]
We present a cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding objects.
Our scheme mitigates the network load in vehicular communication networks and enhances the communication reliability.
arXiv Detail & Related papers (2020-04-23T01:44:12Z)
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.