Learning-Based Safe Control for Robot and Autonomous Vehicle Using Efficient Safety Certificate

Zheng, Haotian; Chen, Chaoyi; Li, S. M.; Zheng, Sifa; Li, Shengbo Eben; Xu, Qing; Wang, Jianqiang

doi:10.1109/ojits.2023.3280573

Cited by 9 publications

(4 citation statements)

References 45 publications

(73 reference statements)

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“…Sensor data plays a critical role in this endeavor by providing valuable insights into the current operational status of autonomous vehicle systems and the in luence of external environmental factors. Such data helps in monitoring and mitigating risks, contributing to the overall safety and reliability of autonomous vehicle technology [11,12].…”

Section: Literature Reviewmentioning

confidence: 99%

Low-Cost Small Scale Autonomous Vehicle

Bogrekci,

Demircioglu,

Goren

2024

JAMRIS

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A low-cost small-scale autonomous vehicle refers to a self-driving vehicle that is affordable and designed for smaller applications or purposes. The firefly algorithm was used to solve obstacle-avoiding problems due to dynamic or static uncertain positioned obstacles. The autonomous vehicle reached the determined position, which could be considered accurate. Regardless of the starting point, it arrived at the defined destination in an area with a diameter of 5 meters. Considering the costs of the sensors, simple algorithm, not very powerful processor and circuit elements, sufficient success has been achieved.

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Section: Literature Reviewmentioning

confidence: 99%

Low-Cost Small Scale Autonomous Vehicle

Bogrekci,

Demircioglu,

Goren

2024

JAMRIS

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“…Haidar et al, [14] used neighbors to classify misbehavior for sybil and replay attacks. Zheng et al, [37] focused on safe performance of autonomous vehicles by proposing learning-based algorithm. Their algorithm enhanced the efficiency and safety of control policies.…”

Section: Review Of Past Studiesmentioning

confidence: 99%

Cyberattack Monitoring Architectures for Resilient Operation of Connected and Automated Vehicles

Khattak,

Smith,

Fontaine

2024

IEEE Open J. Intell. Transp. Syst.

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The deployment of connected and automated vehicles (CAVs) may enhance operations and safety with little human feedback. Automation requires the use of communication and smart devices, thus introducing potential access points for adversaries. This paper develops a prototype real-time monitoring system for a vehicle to infrastructure (V2I) based CAV system that generates cyberattack data for CAV operations under realistic traffic conditions. The monitoring system detects any deviations from the normal operation of CAVs using a long-short term memory (LSTM) neural network proposed by the authors and reverts the system back to a safe state of operation using a set of countermeasures. The proposed algorithm was also compared to convolutional neural network (CNN) and other classical algorithms. The monitoring system detected three different emulated cyberattacks with high accuracy. The LSTM showed the highest accuracy of 98% and outperformed the other algorithms. Further, the performance of the monitoring systems was assessed in terms of the impact on traffic stream stability and safety. The results reveal that a fake basic safety message (BSM) attack on even a single CAV causes the traffic stream to become significantly unstable and increase safety risk without the monitoring system. The monitoring system, however, reverts the system to a safe state of operation and reduces the negative impacts of cyberattacks. The monitoring system improves flow stability by an average of 38% as quantified through acceleration variation and volatility. This is comparable to the base case without attacks. The findings have implications for the design of future resilient systems. INDEX TERMS -Cybersecurity; monitoring system; volatility; cooperative game; connected and automated vehicles; intelligent transportation I. INTRODUCTION Cooperative intelligent transportation technologies including connected and automated vehicles (CAVs) offer the potential to revolutionize transportation systems by improving efficiency and safety [1]. Since driver error is estimated to be a contributing factor in 93% of crashes [2], CAVs may reduce these crashes by reducing the effect of operator errors. CAVs utilize communication between vehicles (V2V) and infrastructure (V2I) to communicate location, position, etc., to surrounding vehicles and roadside units (RSUs). This communication is vital for the wider deployment of CAVs. The operation of multiple CAVs on the road network will require resilient communications between vehicles and infrastructure. This communication dependence provides access points to hackers, however. While vulnerability to cyberattacks also exists in traditional transportation systems [3], the additional reliance and interdependency on communication and automation may exacerbate the situation for CAVs and result in severe crashes and traffic instability. This makes it imperative to analyze the cyber risks of the CAV environment before reaching a mass deployment stage so that the design of these applications can be enhanced to perform...

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“…The aforementioned theoretical and empirical studies indicate that incorporating safety by design would be advantageous for the advancement of autonomous vehicles [49]. However, current approaches primarily concentrate on the mathematical modeling of AVs' responsibilities, assuming that human-driven vehicles will behave as expected [50].…”

Section: Notations Listmentioning

confidence: 99%

Dilemma of Responsibility-Sensitive Safety in Longitudinal Mixed Autonomous Vehicles Flow: A Human-Driver-Error-Tolerant Driving Strategy

2024

IEEE Open J. Intell. Transp. Syst.

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The safety of autonomous vehicles (AVs) is a critical consideration for their widespread adoption. Responsibility sensitive safety (RSS) is proposed to serve as a model checking tool for AV safety. However, RSS alone cannot guarantee safety when they are mixed with human-driven vehicles (HDVs). These HDVs may disregard safety rules, creating dilemmas for AVs where they must choose between crashing into their leader or crashing into their follower. This manuscript defines this dilemma regarding the longitudinal driving and extends it to platooning scenarios with an arbitrary number of vehicles, referred to as polylemma. In polylemma, a violation of safety rules by one vehicle inevitably results in at least one crash between neighboring vehicles. To avoid the polylemmascenario, the manuscript proposes a human error-tolerant (HET) driving strategy, wherein AVs maintain an additional gap and prepare for moderate deceleration to account for potential errors by human drivers. The manuscript derives the risk reduction and capacity variation resulting from the implementation of this strategy at a given market penetration rate (MPR) using real world trajectory data. The analysis indicates that a 50% MPR would reduce risks due to human error by 80%, with a decrease in capacity which vary different for background traffic flow speed.

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Learning-Based Safe Control for Robot and Autonomous Vehicle Using Efficient Safety Certificate

Cited by 9 publications

References 45 publications

Low-Cost Small Scale Autonomous Vehicle

Low-Cost Small Scale Autonomous Vehicle

Cyberattack Monitoring Architectures for Resilient Operation of Connected and Automated Vehicles

Dilemma of Responsibility-Sensitive Safety in Longitudinal Mixed Autonomous Vehicles Flow: A Human-Driver-Error-Tolerant Driving Strategy

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