Dynamic risk assessment in autonomous vehicles motion planning

Wardziński, Andrzej

doi:10.1109/inftech.2008.4621607

Cited by 19 publications

(12 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Related to (1), limitations of observations due to discretization, quantization, and measurement errors are discussed in [17]. Commonly used techniques for dealing with uncertainty in the context of safety monitoring include approaches such as Bayesian method [24], and Dempster-Shafer Logic [2].…”

Section: A Safety Monitorsmentioning

confidence: 99%

Architecting Safety Supervisors for High Levels of Automated Driving

Törngren¹,

Zhang²,

Mohan³

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

The complexity of automated driving poses challenges for providing safety assurance. Focusing on the architecting of an Autonomous Driving Intelligence (ADI), i.e. the computational intelligence, sensors and communication needed for high levels of automated driving, we investigate so called safety supervisors that complement the nominal functionality. We present a problem formulation and a functional architecture of a fault-tolerant ADI that encompasses a nominal and a safety supervisor channel. We then discuss the sources of hazardous events, the division of responsibilities among the channels, and when the supervisor should take over. We conclude with identified directions for further work.

show abstract

Section: A Safety Monitorsmentioning

confidence: 99%

Architecting Safety Supervisors for High Levels of Automated Driving

Törngren¹,

Zhang²,

Mohan³

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

show abstract

“…Their approach is based on DoS attack launched against the robotic vehicle. Wardzinski [ 44 ] proposed a model for an autonomous vehicle control system, which utilizes risk assessment of the current and foreseen situations to plan its movement at an acceptable risk level.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Cyber-Physical Threats on Robotic Platforms

Yousef

AlMajali

Ghalyon

et al. 2018

Sensors

View full text Add to dashboard Cite

Robots are increasingly involved in our daily lives. Fundamental to robots are the communication link (or stream) and the applications that connect the robots to their clients or users. Such communication link and applications are usually supported through client/server network connection. This networking system is amenable of being attacked and vulnerable to the security threats. Ensuring security and privacy for robotic platforms is thus critical, as failures and attacks could have devastating consequences. In this paper, we examine several cyber-physical security threats that are unique to the robotic platforms; specifically the communication link and the applications. Threats target integrity, availability and confidential security requirements of the robotic platforms, which use MobileEyes/arnlServer client/server applications. A robot attack tool (RAT) was developed to perform specific security attacks. An impact-oriented approach was adopted to analyze the assessment results of the attacks. Tests and experiments of attacks were conducted in simulation environment and physically on the robot. The simulation environment was based on MobileSim; a software tool for simulating, debugging and experimenting on MobileRobots/ActivMedia platforms and their environments. The robot platform PeopleBotTM was used for physical experiments. The analysis and testing results show that certain attacks were successful at breaching the robot security. Integrity attacks modified commands and manipulated the robot behavior. Availability attacks were able to cause Denial-of-Service (DoS) and the robot was not responsive to MobileEyes commands. Integrity and availability attacks caused sensitive information on the robot to be hijacked. To mitigate security threats, we provide possible mitigation techniques and suggestions to raise awareness of threats on the robotic platforms, especially when the robots are involved in critical missions or applications.

show abstract

“…These risk analysis methods, however, are computationally expensive as they must be coupled with motion prediction models to improve efficiency. Wardzinski also developed a risk assessment method for motion planning based on physical parameters of a vehicle; however, the study did not include weather conditions and road surface characteristics (35). Laugier et al updated the collision risk estimation method and used hidden Markov models and Gaussian process models to estimate risks as stochastic variables for simple traffic scenarios (36).…”

Section: Risk Analysis Of Autonomous Vehicles In Mixed Traffic Streamsmentioning

confidence: 99%

Risk Analysis of Autonomous Vehicles in Mixed Traffic Streams

Bhavsar

Das

Paugh

et al. 2017

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

The introduction of autonomous vehicles in the surface transportation system could improve traffic safety and reduce traffic congestion and negative environmental effects. Although the continuous evolution in computing, sensing, and communication technologies can improve the performance of autonomous vehicles, the new combination of autonomous automotive and electronic communication technologies will present new challenges, such as interaction with other nonautonomous vehicles, which must be addressed before implementation. The objective of this study was to identify the risks associated with the failure of an autonomous vehicle in mixed traffic streams. To identify the risks, the autonomous vehicle system was first disassembled into vehicular components and transportation infrastructure components, and then a fault tree model was developed for each system. The failure probabilities of each component were estimated by reviewing the published literature and publicly available data sources. This analysis resulted in a failure probability of about 14% resulting from a sequential failure of the autonomous vehicular components alone in the vehicle’s lifetime, particularly the components responsible for automation. After the failure probability of autonomous vehicle components was combined with the failure probability of transportation infrastructure components, an overall failure probability related to vehicular or infrastructure components was found: 158 per 1 million mi of travel. The most critical combination of events that could lead to failure of autonomous vehicles, known as minimal cut-sets, was also identified. Finally, the results of fault tree analysis were compared with real-world data available from the California Department of Motor Vehicles autonomous vehicle testing records.

show abstract

Dynamic risk assessment in autonomous vehicles motion planning

Cited by 19 publications

References 2 publications

Architecting Safety Supervisors for High Levels of Automated Driving

Architecting Safety Supervisors for High Levels of Automated Driving

Analyzing Cyber-Physical Threats on Robotic Platforms

Risk Analysis of Autonomous Vehicles in Mixed Traffic Streams

Contact Info

Product

Resources

About