This paper presents a structured framework for real-time reliability assessment across different estimation paths (topologies) to ensure reliable estimation under fault conditions. Our multi-stage approach first analyzes multiple redundant estimation pathways to effectively isolate fault sources and then reconfigures to the most reliable path based on a analytically defined reliability index. Considering all sensor configurations and independent pathways for estimating vehicle states, the resulting structure forms a directed acyclic graph, termed an estimation graph. A reconfigurable estimation scheme is proposed to enhance reliability across diverse fault conditions. The framework leverages a detailed structural analysis of the estimation graph to enhance fault detectability, as shown by isolating the fault in vertical acceleration. By analytically quantifying fault propagation along each estimation path, the framework enables real-time selection of the optimal path. The proposed analytical formulation offers a unified and computationally efficient approach to quantifying the effects of common soft faults, such as bias and excessive noise. Validation using both high-fidelity CarSim simulations and real-vehicle experiments confirms the accuracy of the analytical derivations and demonstrates the framework's effectiveness in localizing fault sources, ensuring reliable estimation, and enabling real-time reconfiguration under various fault conditions.

The task of making lane change decisions for autonomous vehicles in mixed traffic is intricate and challenging due to the uncertainty of surrounding vehicles. The uncertainty exists in terms of the diverse social driving preferences and unpredictable driving behavior of human drivers. To address these challenges, the decision-making process for changing lanes is represented as an incomplete information game, where the driver characteristics of surrounding vehicles are unknown during the interaction. To eliminate the uncertainty of the driving environment, the concept of driver aggressiveness is proposed to quantify the social driving preferences based on the Risk-Response (R-R) diagram in an explainable manner. Then the predicted trajectory is utilized to calculate the driving risks using Gaussian Mixture Model (GMM) that is trained by the naturalistic driving data in the interactive lane change scenarios extracted from the highD dataset. To make the simulation environment more diverse and realistic, the data-driven motion model social Intelligent Driver Model (SIDM) is constructed based on car-following data obtained from cut-in scenarios in the highD dataset. The simulations are conducted by setting up the environment vehicles equipped with SIDM model with diverse social driving preferences. The findings indicate that the proposed decision-making model can recognize the category of surrounding vehicles, and in realistic interactive driving scenarios, it can produce adaptive and human-like driving decisions.

One of the primary challenges in urban autonomous vehicle decision-making and planning lies in effectively managing intricate interactions with diverse traffic participants characterized by unpredictable movement patterns. Additionally, interpreting and adhering to traffic regulations within rapidly evolving traffic scenarios pose significant hurdles. This paper proposed a rule-based autonomous vehicle decision-making and planning framework which extracts right-of-way from traffic rules to generate behavioural parameters, integrating them to effectively adhere to and navigate through traffic regulations. The framework considers the strong interaction between traffic participants mathematically by formulating the decision-making and planning problem into a differential game. By finding the Nash equilibrium of the problem, the autonomous vehicle is able to find optimal decisions. The proposed framework was tested under simulation as well as full-size vehicle platform, the results show that the ego vehicle is able to safely interact with surrounding traffic participants while adhering to traffic rules.

Functional integration is a growing trend in vehicle control, often involving the coordination of multiple controllers to achieve various objectives simultaneously. The need for flexibility and reliability has led to a “plug-and-play” approach in control system design, which presents challenges for traditional integrated model predictive control (MPC). Agent-based model predictive control (AMPC) has recently emerged as a distributed solution that treats controllers as agents, creating a collaborative framework among them to reach a common goal. However, this approach struggles to manage distributed conflicting objectives when agents are coupled or interdependent. To address this, we propose a novel, practical distributed control scheme called multi-objective AMPC, which adapts the alternating direction method of multipliers (ADMM) into a general control strategy that approximates global optimization while decoupling objectives. We systematically develop three formulations that maintain convergence while addressing control regularization and inequality constraints, applying them to complex vehicle control systems for the first time. The proposed method has been tested on two vehicle control scenarios with a multi-objective topology. Different formulations are compared through simulations, and the most computationally efficient one was implemented on an electric vehicle for real-world evaluations. The results demonstrate that the proposed multi-objective AMPC can converge approximately to the same global optimum as integrated MPC with greater flexibility and the potential to reduce computational costs.

Autonomous techniques are becoming increasingly integrated into our daily lives. Many advanced driver assistance systems (ADAS), including functions like lane-keeping assist and car following, are already implemented in vehicles for controlled environments such as highways. However, to enhance the capabilities of current ADAS, it is essential to extend their application to more general scenarios, like urban driving. Urban environments pose considerable challenges due to the high density of traffic participants, including pedestrians and cyclists, whose behaviors are unpredictable and necessitate strong interactions with self-driving vehicles. Addressing these complex interactions through real-time decision-making is particularly challenging but crucial for effective operation in real-world urban settings. This paper aims to bring the decision-making process of autonomous driving techniques closer to real life by proposing a motion planning and decision-making framework that utilizes game theory to formulate and consider strong interactions. Additionally, we introduce a human-like attention-based traffic actor filter to enable the autonomous vehicle to focus on critical traffic participants with a higher risk of collision. The framework is tested in both simulation and real-world scenarios, demonstrating that the algorithm can make safe and efficient decisions under various traffic scenarios involving multiple types of traffic participants in real time.

This paper proposes a hybrid model/data fault detection and diagnosis system applicable to any vehicle sensor and actuator. This system works based on comparing the measurements of a target sensor or the desired control actions of a target actuator with their estimations. These estimations are obtained by a hybrid estimator developed based on the integration of model-based and data-driven estimators leveraging the strength of each estimator. Considering the weakness of pure data-driven estimators in confronting unknown conditions, a self-updating dataset is proposed to learn new cases. After fault detection, the estimations of the hybrid estimator are used to reconstruct sensor data or find the level of actuator failure. To evaluate the performance of the proposed hybrid fault detection and diagnosis system, it is applied to a vehicle’s lateral acceleration sensor and traction motor. The results of experimental tests conducted on an all-wheel-drive vehicle show the effectiveness of the algorithm in detecting and quantifying faults in the target component.

Agent-based model predictive control (AMPC) has recently been proposed as a distributed scheme that collaborates with all agents to achieve optimal holistic performance. However, its optimality highly depends on the prediction accuracy that requires all agents or their contributions to be known, which is too idealistic for actual implementation. This research proposes a novel practical hybrid control scheme - learning agent-based MPC (LAMPC), combining the model-based AMPC approach and data-based learning methods to improve the holistic vehicle performance for multi-agent systems. The Gaussian process regression (GPR) enhanced by an online data management strategy serves as the learning core to predict unknown contributions. A novel multi-step prediction mechanism leverages the GPR learning potential along the horizon. The predicted mean, representing the learned unknown contributions, completes the system model in the MPC for more accurate control. Meanwhile, a stochastic framework is formulated to guarantee control safety and feasibility using soft chance constraints based on the prediction variance. Both simulations and experiments show that, with the learning capability, LAMPC outperforms the traditional AMPC. LAMPC can achieve higher tracking performance in well-learned scenarios and always guarantee constraint satisfaction even in less-learned scenarios. Moreover, the proposed hybrid control scheme is efficient for real-time implementation and is flexible to any control agent topology.

Left turning for autonomous vehicles at intersections is challenging due to the various driving behaviors from different human drivers and the strong interaction between the autonomous vehicle and human traffic participants. This paper proposes a planning and decision making framework for intersection left-turning which considers the interaction between autonomous vehicles and human drivers as well as pedestrians to address this issue. The proposed framework considers interactions mathematically by formulating the problem as a linear quadratic differential game. Through solving the Nash equilibrium of the game, the autonomous vehicle is able to properly interact with surrounding traffic participants. Under the differential game framework, the accuracy of the interaction formulation is closely related to the behavior model of human drivers. Therefore, real-world human behavior is extracted and evaluated from naturalistic driving dataset to help establish more realistic modeling and estimation of various kinds of traffic participants, including aggressive, neutral and conservative traffic participants. The simulation results show that the autonomous vehicle is able to properly estimate the types of traffic participants by observing their behavior using the proposed technique. Then the autonomous vehicle behave according to the types of those traffic participants to enable interactive and human-like planning and decision making at intersections.

In this paper, we develop a framework against inference attacks aimed at inferring the values of the controller gains of an active steering control system (ASCS). We first show that an adversary with access to the shared information by a vehicle, via a vehicular ad hoc network (VANET), can reliably infer the values of the controller gains of an ASCS. This vulnerability may expose the driver as well as the manufacturer of the ASCS to severe financial and safety risks. To protect controller gains of an ASCS against inference attacks, we propose a randomized filtering framework wherein the lateral velocity and yaw rate states of a vehicle are processed by a filter consisting of two components: a nonlinear mapping and a randomizer. The randomizer randomly generates a pair of pseudo gains which are different from the true gains of the ASCS. The nonlinear mapping performs a nonlinear transformation on the lateral velocity and yaw rate states. The nonlinear transformation is in the form of a dynamical system with a feedforward-feedback structure which allows real-time and causal implementation of the proposed privacy filter. The output of the filter is then shared via the VANET. The optimal design of randomizer is studied under a privacy constraint that determines the protection level of controller gains against inference attacks, and is in terms of mutual information. It is shown that the optimal randomizer is the solution of a convex optimization problem. By characterizing the distribution of the output of the filter, it is shown that the statistical distribution of the filter’s output depends on the pseudo gains rather than the true gains. Using information-theoretic inequalities, we analyze the inference ability of an adversary in estimating the control gains based on the output of the filter. Our analysis shows that the performance of any estimator in recovering the controller gains of an ASCS based on the output of the filter is limited by the privacy constraint. The performance of the proposed privacy filter is compared with that of an additive noise privacy mechanism. Our numerical results show that the proposed privacy filter significantly outperforms the additive noise mechanism, especially in the low distortion regime.

This paper presents a comprehensive study on the impact of information flow topologies on the resilience of distributed algorithms that are widely used for estimation and control in vehicle platoons. In the state of the art, the influence of information flow topology on both internal and string stability of vehicle platoons has been well studied. However, understanding the impact of information flow topology on cyber-security tasks, e.g., attack detection, resilient estimation and formation algorithms, is largely open. By means of a general graph theory framework, we study connectivity measures of several platoon topologies and we reveal how these measures affect the ability of distributed algorithms to reject communication disturbances, to detect cyber-attacks, and to be resilient against them. We show that the traditional platoon topologies relying on interaction with the nearest neighbor are very fragile with respect to performance and security criteria. On the other hand, appropriate platoon topologies, namely$k$-nearest neighbor topologies, are shown to fulfill desired security and performance levels. The framework we study covers undirected and directed topologies, ungrounded and grounded topologies, or topologies on a line and on a ring. We show that there is a trade-off in the network design between the robustness to disturbances and the resilience to adversarial actions. Theoretical results are validated via simulations.

A novel cyber physical method is proposed and experimentally verified for reliable distributed estimation of vehicle longitudinal velocity, robustly to road friction condition variations. In this method, the vehicle speed estimated at each of the four corners of the vehicle, using a linear parameter-varying observer in the physical layer, and speed data measured by a conventional low-cost GPS are incorporated in a distributed structure (in the cyber layer) to enhance the reliability of the estimate. The method minimizes a cost function quantizing the effect of disturbances on each corner's estimation and adversaries due to occasional GPS signal drops. A fault-tolerant estimation policy is integrated to deal with large deviations in corner estimations, which have unexpectedly high levels of confidence. The main advantages of the proposed method are increased reliability on various road surface conditions and robustness to faults, as confirmed by road tests. Several experimental tests, including lane change and low-excitation maneuvers, with various powertrain configurations on dry and slippery roads demonstrate the efficiency of the algorithm.

Reliable estimation of vehicle speed is an active topic of research in the automotive industry and academia due to its technical challenges as well as applications to vehicle traction and stability control. In this direction, the emergence of new generations of communication technologies has brought new perspectives to traditional studies on vehicle speed estimation and control. To this end, this paper introduces a cooperative vehicle speed fault diagnosis and correction algorithm. The distributed part of the algorithm is based on a distributed function calculation algorithm for vehicle networks. The introduced algorithm enables each vehicle to gather some information from other vehicles in the network in a distributed manner and is robust to communication failures. A procedure to use such information for a single vehicle to diagnose and correct a possible fault in its own speed estimation/measurement is discussed. The functionality and performance of the proposed algorithms are verified via illustrative examples and simulation results.

An opinion dynamics approach is proposed to enhance the reliability of the vehicle velocity estimators, which are required for autonomous driving as well as advanced vehicle active safety systems, such as traction and stability control. The corners' estimates of a velocity observer, which is formed by combining the kinematic and model-based estimation schemes, are used as opinions with different levels of confidence in the developed algorithm. This is to find more reliable estimates robust to disturbances and time delay via solving a convex optimization problem. To bypass the effect of failure in velocity estimation, a fault rejection policy is used concurrently with the opinion dynamics. Road tests confirm the validity and robustness of the algorithm on slippery and dry roads independent of the powertrain configuration in different driving scenarios, especially for combined-slip and low-excitation maneuvers, which are demanding for the current vehicle state estimators.

A distributed estimation approach based on opinion dynamics is proposed to enhance the reliability of vehicle corners' velocity estimates. The corners' estimates, which are obtained from a Kalman filter, is formed by integrating the model-based and kinematic-based velocity estimation approaches. These estimates are utilized as opinions with different levels of confidence in the developed algorithm. More reliable estimates robust to disturbances and time delay are achieved via solving a convex optimization problem. Vehicle tests with various driveline configurations are performed to verify the estimator performance under different surfaces friction conditions in pure and combined-slip (combination of longitudinal/lateral) maneuvers, which are arduous for the current vehicle state estimators.

We consider a graph-theoretic approach to the performance and robustness of a platoon of vehicles, in which each vehicle communicates with its k-nearest neighbors. In particular, we quantify the platoon's stability margin, robustness to disturbances (in terms of system H∞ norm), and maximum delay tolerance via graph-theoretic notions, such as nodal degrees and (grounded) Laplacian matrix eigenvalues. The results show that there is a trade-off between robustness to time delay and robustness to disturbances. Both lurst-order dynamics (reference velocity tracking) and second-order dynamics (controlling inter-vehicular distance) are analyzed in this direction. Theoretical contributions are conlurmed via simulation results.

This paper proposes a model predictive collision avoidance scheme for use in autonomous driving, based on cooperative on-line estimation of unknown and time varying road conditions. The autonomous vehicle is linearly modelled with constraints dependent on the road condition parameter. The proposed model predictive controller (MPC) is designed to be adaptive to this parameter. To accommodate this adaptive design, a particular method is developed for estimating the road friction coefficient cooperatively, by disseminating individual estimates in a vehicular network and using a consensus algorithm to converge these estimates to the maximum likelihood value. Presented simulation results demonstrate that the cooperative consensus scheme improves estimation significantly, and accordingly, the adaptive MPC incorporates road condition properly in collision avoidance planning.