arXiv subjects

Yunyi Jia

Publications and source records attributed to Yunyi Jia.

11 recordsLinked to original sources

arXiv2026

Mitigating Degradation Attacks in Cooperative Autonomous Driving via Intention Sharing: A Vehicle-in-the-Loop Study

Communication delays induced by cyber attacks present a critical challenge to the safe operation of connected autonomous driving. This study investigates the use of intention sharing communication strategy to enhance the resilience of model predictive controllers under Denial-of-Service attacks. We employ a vehicle-in-the-loop testbed integrating a real drive-by-wire vehicle with a microscopic traffic simulator and vehicle-to-X communication infrastructure. We emulate Denial-of-service attacks that induce communication delays of up to two seconds. We evaluate three controller variants: baseline status-sharing, intention-sharing, and delay-aware intention-sharing control. Experimental results reveal that while baseline control suffers significant performance degradation and frequent collisions under adversarial delay, intention sharing eliminates collisions and maintains behavior near nominal levels for the tested scenarios. These findings demonstrate the practical potential of intention-sharing architectures for safeguarding connected vehicles against network-layer degradation.

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arXiv2026

Classically Realizable Incompatibility

Incompatibility constitutes a fundamental aspect of quantum mechanics. However, not every quantum observable non-classical property arises from incompatibility, nor can all quantum scenarios be fully captured by incompatibility alone. Within the framework of partial Boolean algebra (pBA), we research the structural properties of incompatibility scenarios. We introduce a unified method to realize any incompatibility scenario via a classical game, and the construction is extendable to any scenario embeddable into a Boolean algebra. The exclusivity graph offers a precise characterization of incompatibility scenarios. We prove that every exclusivity graph is the atom graph of an exclusive pBA, which is embedded into a Boolean algebra. These results provide a necessary condition for exclusivity graphs and a sufficient condition for atom graphs.

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arXiv2026

Hardy's Paradox for Yu-Oh Set Constructed by Logically Contextual Quantum States

Quantum contextuality is a fundamental nonclassical property of quantum systems, regarded as a key resource that demonstrates the computational and informational advantages of quantum over classical systems. Our present work aims to construct Hardy's paradoxes, a set of possibilistic conditions witnessing contextuality, for Yu-Oh set, which is the state-independent contextual quantum system with the least number of vectors. To achieve the aim, we systematically enumerate all logically contextual pure states on Yu-Oh set, and theoretically prove that no mixed states in this scenario are logically contextual. Based on the identified logically contextual quantum states, we construct 12 Hardy's paradoxes with identical success probability SP=11.1%. Furthermore, we present corresponding observables to experimentally witness these Hardy's paradoxes.

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arXiv2026

Actor-Critic Cooperative Compensation to Model Predictive Control for Off-Road Autonomous Vehicles Under Unknown Dynamics

This study presents an Actor-Critic Cooperative Compensated Model Predictive Controller (AC3MPC) designed to address unknown system dynamics. To avoid the difficulty of modeling highly complex dynamics and ensuring realtime control feasibility and performance, this work uses deep reinforcement learning with a model predictive controller in a cooperative framework to handle unknown dynamics. The model-based controller takes on the primary role as both controllers are provided with predictive information about the other. This improves tracking performance and retention of inherent robustness of the model predictive controller. We evaluate this framework for off-road autonomous driving on unknown deformable terrains that represent sandy deformable soil, sandy and rocky soil, and cohesive clay-like deformable soil. Our findings demonstrate that our controller statistically outperforms standalone model-based and learning-based controllers by upto 29.2% and 10.2%. This framework generalized well over varied and previously unseen terrain characteristics to track longitudinal reference speeds with lower errors. Furthermore, this required significantly less training data compared to purely learning-based controller, while delivering better performance even when under-trained.

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arXiv2026

Reinforcement Learning Compensated Model Predictive Control for Off-road Driving on Unknown Deformable Terrain

This study presents an Actor-Critic reinforcement learning Compensated Model Predictive Controller (AC2MPC) designed for high-speed, off-road autonomous driving on deformable terrains. Addressing the difficulty of modeling unknown tire-terrain interaction and ensuring real-time control feasibility and performance, this framework integrates deep reinforcement learning with a model predictive controller to manage unmodeled nonlinear dynamics. We evaluate the controller framework over constant and varying velocity profiles using high-fidelity simulator Project Chrono. Our findings demonstrate that our controller statistically outperforms standalone model-based and learning-based controllers over three unknown terrains that represent sandy deformable track, sandy and rocky track and cohesive clay-like deformable soil track. Despite varied and previously unseen terrain characteristics, this framework generalized well enough to track longitudinal reference speeds with the least error. Furthermore, this framework required significantly less training data compared to purely learning based controller, converging in fewer steps while delivering better performance. Even when under-trained, this controller outperformed the standalone controllers, highlighting its potential for safer and more efficient real-world deployment.

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arXiv2026

A Logical Formalism of Hardy-type Paradox

Hardy-type paradoxes provide elegant, inequality-free proofs of quantum contextuality. We introduce a unified logical formalism for these paradoxes, termed logical Hardy-type paradoxes. For any finite quantum scenario of ideal measurements, we prove that the existence of a logical Hardy-type paradox is equivalent to logical contextuality. Specifically, strong contextuality is equivalent to logical Hardy-type paradoxes with success probability SP = 1. These results generalize prior work on (2,k,2), (2,2,d), and n-cycle scenarios. We analyze logical Hardy-type paradoxes in the Mansfield and Klyachko-Can-Binicioglu-Shumovsky (KCBS) scenarios. In the KCBS scenario, we show that there is exactly one type of logical Hardy-type paradox, achieving SP\approx 10.56% for a specific parameter setting.

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arXiv2025

Event-based quantum contextuality theory

Fully revealing the mathmatical structure of quantum contextuality is a significant task, while some known contextuality theories are only applicable for rank-1 projectors. That is because they adopt the observable-based definitions. This paper overcomes the challenges faced by some known contextuality theories by establishing an event-based contextuality theory with exclusive partial Boolean algebra, which is used to describe the contextual systems with local consistency and exclusivity principle. Our theory provides a precise mathematical framework for quantum contextuality, which can handle the scenarios composed of general projectors, and introduces a more complete contextuality hierarchy. We conclude that the Kochen-Specker contextuality is equivalent to the state-independent strong contextuality for finite dimensional quantum systems. Therefore, when considering both the strength and proportion of contextual quantum states, Kochen-Specker contextuality is the strongest.

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arXiv2024

Energy Efficient Automated Driving as a GNEP: Vehicle-in-the-loop Experiments

In this paper, a multi-agent motion planning problem is studied aiming to minimize energy consumption of connected automated vehicles (CAVs) in lane change scenarios. We model this interactive motion planning as a generalized Nash equilibrium problem and formalize how vehicle-to-vehicle intention sharing enables solution of the game between multiple CAVs as an optimal control problem for each agent, to arrive at a generalized Nash equilibrium. The method is implemented via model predictive control (MPC) and compared with an advanced baseline MPC which utilizes unilateral predictions of other agents' future states. A ROS-based in-the-loop testbed is developed: the method is first evaluated in software-in-the-loop and then vehicle-in-the-loop experiments are conducted. Experimental results demonstrate energy and travel time benefits of the presented method in interactive lane change maneuvers.

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arXiv2022

Energy-Efficient Driving in Connected Corridors via Minimum Principle Control: Vehicle-in-the-Loop Experimental Verification in Mixed Fleets

Connected and automated vehicles (CAVs) can plan and actuate control that explicitly considers performance, system safety, and actuation constraints in a manner more efficient than their human-driven counterparts. In particular, eco-driving is enabled through connected exchange of information from signalized corridors that share their upcoming signal phase and timing (SPaT). This is accomplished in the proposed control approach, which follows first principles to plan a free-flow acceleration-optimal trajectory through green traffic light intervals by Pontryagin's Minimum Principle in a feedback manner. Urban conditions are then imposed from exogeneous traffic comprised of a mixture of human-driven vehicles (HVs) - as well as other CAVs. As such, safe disturbance compensation is achieved by implementing a model predictive controller (MPC) to anticipate and avoid collisions by issuing braking commands as necessary. The control strategy is experimentally vetted through vehicle-in-the-loop (VIL) of a prototype CAV that is embedded into a virtual traffic corridor realized through microsimulation. Up to 36% fuel savings are measured with the proposed control approach over a human-modelled driver, and it was found connectivity in the automation approach improved fuel economy by up to 26% over automation without. Additionally, the passive energy benefits realizable for human drivers when driving behind downstream CAVs are measured, showing up to 22% fuel savings in a HV when driving behind a small penetration of connectivity-enabled automated vehicles.

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arXiv2021

Adaptive Optimization of Autonomous Vehicle Computational Resources for Performance and Energy Improvement

Autonomous vehicles usually consume a large amount of computational power for their operations, especially for the tasks of sensing and perception with artificial intelligence algorithms. Such a computation may not only cost a significant amount of energy but also cause performance issues when the onboard computational resources are limited. To address this issue, this paper proposes an adaptive optimization method to online allocate the onboard computational resources of an autonomous vehicle amongst multiple vehicular subsystems depending on the contexts of the situations that the vehicle is facing. Different autonomous driving scenarios were designed to validate the proposed approach and the results showed that it could help improve the overall performance and energy consumption of autonomous vehicles compared to existing computational arrangement.

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arXiv2020

Energy and Flow Effects of Optimal Automated Driving in Mixed Traffic: Vehicle-in-the-Loop Experimental Results

This paper experimentally demonstrates the effectiveness of an anticipative car-following algorithm in reducing energy use of gasoline engine and electric Connected and Automated Vehicles (CAV), without sacrificing safety and traffic flow. We propose a Vehicle-in-the-Loop (VIL) testing environment in which experimental CAVs driven on a track interact with surrounding virtual traffic in real-time. We explore the energy savings when following city and highway drive cycles, as well as in emergent highway traffic created from microsimulations. Model predictive control handles high level velocity planning and benefits from communicated intentions of a preceding CAV or estimated probable motion of a preceding human driven vehicle. A combination of classical feedback control and data-driven nonlinear feedforward control of pedals achieve acceleration tracking at the low level. The controllers are implemented in ROS and energy is measured via calibrated OBD-II readings. We report up to 30% improved energy economy compared to realistically calibrated human driver car-following without sacrificing following headway.

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