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An intermittent, model-free optimal control algorithm that enables an autonomous vehicle to track a nonpredetermined trajectory at high speed is presented. The approachisbandwidthandenergyefficientinthatcommunication between actuators is limited to instances when it is needed rather than performing unnecessary periodic updates. We formulate the problem by properly augmenting the system and reference (trajectory) data, and then designing a triggering mechanism for the controller to work with a sampled version of the augmented states at some triggering instants. In order to obtain a model-free solution, we leverage a Q-learning framework with a zero-order hold actor network and a critic network to approximate the optimal intermittent controller and the optimal cost, respectively, resulting in appropriate tuning laws. Finally, we provide a numerical example of an ground vehicle driving autonomously at high-speed on a race track. 

Schedule randomization is one of the recently introduced security defenses against schedule-based attacks, i.e., attacks whose success depends on a particular ordering between the execution window of an attacker and a victim task within the system. It falls into the category of information hiding (as opposed to deterministic isolation-based defenses) and is designed to reduce the attacker's ability to infer the future schedule. This paper aims to investigate the limitations and vulnerabilities of schedule randomization-based defenses in real-time systems. We first provide definitions, categorization, and examples of schedule-based attacks, and then discuss the challenges of employing schedule randomization in real-time systems. Further, we provide a preliminary security test to determine whether a certain timing relation between the attacker and victim tasks will never happen in systems scheduled by a fixed-priority scheduling algorithm. Finally, we compare fixed-priority scheduling against schedule-randomization techniques in terms of the success rate of various schedule-based attacks for both synthetic and real-world applications. Our results show that, in many cases, schedule randomization either has no security benefits or can even increase the success rate of the attacker depending on the priority relation between the attacker and victim tasks.