Timing Analysis of CAN FD for Security-Aware Automotive Cyber-Physical Systems

Xie, Yong; Zeng, Gang; Kurachi, Ryo; Xiao, Fu; Takada, Hiroaki

doi:10.1109/tdsc.2022.3194712

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…On the one hand, there is a lack of interpretability for deploying reward functions for deep reinforcement learning because the state and action space inputs for reinforcement learning come from a network of black boxes. The reward function allows the combination of prior knowledge [ 1 ]; however, reward functions based on prior human knowledge are typically subjective and sparse and thus cannot accurately represent the task goal [ 2 ]. Subjectivity, in other words, leads to reward functions that may focus on specific explicit criteria while ignoring the relationship between specific states and task goals.…”

Section: Introductionmentioning

confidence: 99%

Safe Decision Controller for Autonomous DrivingBased on Deep Reinforcement Learning inNondeterministic Environment

Chen

Bhatti

Huang

2023

Sensors

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Autonomous driving systems are crucial complicated cyber–physical systems that combine physical environment awareness with cognitive computing. Deep reinforcement learning is currently commonly used in the decision-making of such systems. However, black-box-based deep reinforcement learning systems do not guarantee system safety and the interpretability of the reward-function settings in the face of complex environments and the influence of uncontrolled uncertainties. Therefore, a formal security reinforcement learning method is proposed. First, we propose an environmental modeling approach based on the influence of nondeterministic environmental factors, which enables the precise quantification of environmental issues. Second, we use the environment model to formalize the reward machine’s structure, which is used to guide the reward-function setting in reinforcement learning. Third, we generate a control barrier function to ensure a safer state behavior policy for reinforcement learning. Finally, we verify the method’s effectiveness in intelligent driving using overtaking and lane-changing scenarios.

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Section: Introductionmentioning

confidence: 99%

Safe Decision Controller for Autonomous DrivingBased on Deep Reinforcement Learning inNondeterministic Environment

Chen

Bhatti

Huang

2023

Sensors

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“…Once the malicious message overwrites the legitimate one, the attacker can control the associated operation. It can cause serious consequences if the malicious message involves the brake system, the powertrain, or the safety controls of the target vehicle [4].…”

Section: Introductionmentioning

confidence: 99%