Robust control strategy for platoon of connected and autonomous vehicles considering falsified information injected through communication links | Semantic Scholar (2024)

Results indicate that the proposed cooperative control strategy can produce accurate estimations of the number and the kinematic states of HDVs between CAVs and achieve string stability while maintaining smaller time headways, compared to car‐following models used for training guidance under different market penetration rates of CAVs, which significantly improves traffic smoothness and mobility.

This study develops a general framework for modeling and synthesizing two types of candidate attacks on ACC vehicles, namely direct attacks on vehicle control commands and false data injection attacks on sensor measurement, with explicit characterization of their adverse effects.

A communication topology anomaly response system to ensure platoon safety, which consists of a trigger module and a control module, and demonstrates the remarkable effectiveness of CTARS in safeguarding the security of CAVP during cyberattacks, thus confirming its exceptional performance.

The primary findings indicate that strategically synthesized candidate attacks can cause significant disruptions to the traffic flow while altering the driving behavior of ACC vehicles in a subtle fashion to remain stealthy, which is supported by a series of analytical results.

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This paper focuses in detecting replay attack in a connected vehicle system under Cooperative Adaptive Cruise Control (CACC) where one of the vehicle is affected by a malicious agent which wants to compromise the safety and the performance of the platoon.

This work proposes a novel detection technique based on a state estimator that takes advantage of communicated data from multiple vehicles connected over VANET to cope with the problem of detecting stealthy attacks on communicated data and the control algorithm in cooperative vehicle platoons.

A smooth-switching control-based CACC scheme with IFT optimization (CACC-SOIFT) by implementing a bi-layer optimization model and a Kalman predictor that can guarantee string stability and riding comfort in the environment of dynamic IFT.

A Human-Lead-Platoon CACC ((HLP-CACC) controller is proposed for connected and automated vehicles to “include” human drivers in platooning process so that turbulence caused by human drivers could be smoothed out by automated vehicles.

A novel consensus-based control strategy, for platoons of autonomous vehicles, able to counteract to message falsification attacks, is proposed, and comprehensive analysis confirms the effectiveness of the proposed solution.

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