Safe and Secure Control of Connected and Automated Vehicles: An Event-Triggered Control Approach Using Trust-Aware Robust Control Barrier Functions

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to safely navigate through a conflict area (e.g., traffic intersections, merging roadways, roundabouts). Previous studies have shown that such a network can be targeted by adversarial attacks causing traffic jams or safety violations ending in collisions. We focus on attacks targeting the V2X communication network used to share vehicle data and consider as well uncertainties due to noise in sensor measurements and communication channels. To combat these, motivated by recent work on the safe control of CAVs, we propose a trust-aware robust event-triggered decentralized control and coordination framework that can provably guarantee safety. We maintain a trust metric for each vehicle in the network computed based on their behavior and used to balance the tradeoff between conservativeness (when deeming every vehicle as untrustworthy) and guaranteed safety and security. It is important to highlight that our framework is invariant to the specific choice of the trust framework. Based on this framework, we propose an attack detection and mitigation scheme which has twofold benefits: (i) the trust framework is immune to false positives, and (ii) it provably guarantees safety against false positive cases. We use extensive simulations (in SUMO and CARLA) to validate the theoretical guarantees and demonstrate the efficacy of our proposed scheme to detect and mitigate adversarial attacks. The code for the simulated scenarios can be found in this link.

<div class="section abstract"><div class="htmlview paragraph">The development of connected and automated vehicles (CAVs) is rapidly increasing in the next generation and the automotive industry is dedicated to enhancing the safety and efficiency of CAVs. A cooperative control strategy helps CAVs to collaborate and share information among the neighboring CAVs, improving efficiency, optimizing traffic flow, and enhancing safety. This research proposes a safe cooperative control framework for CAVs designed for highway merging applications. In the urban transportation system, highway merging scenarios are high-risk collision zone, and the CAVs on the main and merging lanes should collaborate to avoid potential accidents. In the proposed framework, the on-ramp CAVs merge at 40 mph within the same and opposite directions to the main lane CAVs. The proposed framework includes the consensus controller, safety filter, and quadratic programming (QP) optimization method. The consensus controller incorporates the communication between CAVs and designs the same consensus for all CAVs on both the main and merging lanes. However, when all CAVs try to achieve the same consensus, a potential collision might happen on the road. The safety filter is a crucial part of controller that requires the location, velocity, and safe distance information among CAVs to calculate the safe set for the controller. To balance both the consensus controller and the safety filter, QP is applied to have a safe, cooperative control input for all CAVs. The same and opposite merging scenarios are common in urban transportation systems, so we chose these two scenarios to validate the framework. In these scenarios, the cooperative control can avoid all the potential collision points and maintain the desired safe distance from neighboring CAVs. The simulation results demonstrate the effectiveness of the proposed safe cooperative control framework for complex highway merging scenarios considering safety criteria like time to collision (TTC), safety margin.</div></div>

This paper considers the heterogeneous traffic intersection where both Human Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) exist. In such a dynamic environment, CAVs must act in a way such that safety is guaranteed at all times, which is challenging due to the unpredictable nature of human behavior. To guarantee safety, in this paper we consider the worst-case behavior of HDVs by constructing the forward reachable set and ensuring collision avoidance against the forward reachable set within the CAV's planning horizon. To ensure safety at all times, a maximal invariant safe set is designed and used as a terminal constraint such that within this set there is always admissible control for CAVs to react against all possible future behavior of other vehicles safely. Finally, we propose to solve the intersection coordination problem within a Distributed Model Predictive Control (DMPC) framework where all pairwise safety constraints among CAVs are decoupled by prioritization. As a result, each CAVs solves a Mixed Integer Quadratic Programming (MIQP) problem considering collision avoidance with all CAVs of higher priority and with all HDVs. We give theoretical proof of the recursive feasibility of our proposed DMPC formulation and practical invariant safety guarantees. The resulting solution is evaluated in simulation and shows that our coordination framework can provide invariant safe coordination in a heterogeneous traffic intersection.

As typical applications of cyber–physical systems (CPSs), connected and automated vehicles (CAVs) are able to measure the surroundings and share local information with the other vehicles by using multimodal sensors and wireless networks. CAVs are expected to increase safety, efficiency, and capacity of our transportation systems. However, the increasing usage of sensors has also increased the vulnerability of CAVs to sensor faults and adversarial attacks. Anomalous sensor values resulting from malicious cyberattacks or faulty sensors may cause severe consequences or even fatalities. In this article, we increase the resilience of CAVs to faults and attacks by using multiple sensors for measuring the same physical variable to create redundancy. We exploit this redundancy and propose a sensor fusion algorithm for providing a robust estimate of the correct sensor information with bounded errors independent of the attack signals, and for attack detection and isolation. The proposed sensor fusion framework is applicable to a large class of security-critical CPSs. To minimize the performance degradation resulting from the usage of the estimation for control, we provide an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> controller for cooperative adaptive cruise control-equipped CAVs. The designed controller is capable of stabilizing the closed-loop dynamics of each vehicle in the platoon while reducing the joint effect of estimation errors and communication channel noise on the tracking performance and string behavior of the vehicle platoon. Numerical examples are presented to illustrate the effectiveness of our methods.

Adversarial attacks have received much attention as communication network applications rise in popularity. Connected and Automated Vehicles (CAVs) must be protected against adversarial attacks to ensure passenger and vehicle safety on the road. Nevertheless, CAVs are susceptible to several types of attacks, such as those that target intra- and inter-vehicle networks. These harmful attacks not only cause user privacy and confidentiality to be lost, but they also have more grave repercussions, such as physical harm and death. It is critical to precisely and quickly identify adversarial attacks to protect CAVs. This research proposes (1) a new dataset comprising three adversarial attacks in the CAV network traffic and normal traffic, (2) a two-phased adversarial attack detection technique named TAAD-CAV, where in the first phase, an ensemble voting classifier having three machine learning classifiers and one separate deep learning classifier is trained, and the output is used in the next phase. In the second phase, a meta classifier (i.e., Decision Tree is used as a meta classifier) is trained on the combined predictions from the previous phase to detect adversarial attacks. We preprocess the dataset by cleaning data, removing missing values, and adjusting the Z-score normalization. Evaluation metrics such as accuracy, recall, precision, F1-score, and confusion matrix are employed to evaluate and compare the performance of the proposed model. Results reveal that TAAD-CAV achieves the highest accuracy with a value of 70% compared with individual ML and DL classifiers.

In this work, the connected vehicle's messages are used to create an enhanced adaptive traffic signal control (ATSC) system for improved traffic flow. Few existing studies use connected and automated vehicles (CAVs) to develop traffic signal control algorithms under hybrid connected and autonomous conditions. The proposed approach focuses on a four‐phase traffic intersection with both CAVs and human‐driven vehicles (HVs). CAVs share real‐time state information, and a model called Roads Dynamic Segmentation estimates queuing procedures and vehicle fleet numbers on dynamic road sections. This information is used in the Store and Forward Model (SFM) to predict intersection queuing length. The ATSC system, based on model predictive control (MPC), aims to minimize intersection queue length while considering traffic constraints (undersaturated, saturated, and oversaturated) and avoiding free‐flow problems due to queue overflow. To reduce computational complexity, a linear‐quadratic‐regulator (LQR) is used. Real‐world vehicle trajectories and the SUMO tool are used for experimental purposes. Results show that the proposed method reduces average delay by 38.50% and 33.42% compared to fixed timing and traditional MPC in cases of oversaturated traffic flow with 100% CAV penetration. Even with a penetration rate of only 20%, average delay decreases by 13.65% and 6.50%, respectively. This study showcases not only the potential benefits of CAV in enhancing traffic, but also enables the optimal utilization of green duration in signalized intersection control systems. This helps prevent traffic congestion and ensures the efficient and smooth movement of traffic flow.

A spatiotemporal control method at isolated intersections under mixed‐autonomy traffic conditions

Connected and Automated Vehicles (CAVs) are projected to dominate traffic roads in the future due to their potential advantages in efficiency and safety. CAVs are equipped with sensors and onboard computers that allows them to perform coordination. The transition toward fully autonomous era will see a gradual replacement of legacy Human-Driven Vehicles (HDVs) creating mixed traffic environments. In such environments, the presence of HDVs can pose challenges to CAVs due to their uncertain behaviors and intentions. The particular concern of CAVs-HDVs interactions occurs at traffic intersections, where these road segments are responsible for the highest share of traffic jams and fatalities. Additionally, vehicle coordination in mixed traffic involves computationally difficult problems that cannot be solved in a tractable way. This thesis presents optimization-based coordination strategies which builds upon mixed-platooning scheme and heuristic approaches. By utilizing the CAVs presence, the platooning strategy is implemented to partially control the HDVs. To retrieve initial intersection crossing order, a feasibility-enforcing Alternating Direction Methods of Multipliers (ADMM) is employed. Furthermore, an optimization-based heuristic is developed to efficiently evaluate reordering scenarios. The heuristic employs constraint-feasibility check and cost comparison techniques. Next, in an economic optimal coordination scenario, a sensitivity-based heuristic is implemented to further reduce computational loads by approximating Nonlinear Program (NLP) solutions. The numerical results demonstrate that these heuristics can achieve near-optimal solutions and be better than the alternatives while can be hundred times faster than the Mixed-Integer Program (MIP) solvers.

Vehicle platooning is an emerging and promising technology with the benefit of fuel-saving and traffic capacity improvement, but the presence of long platoons near merging roads could act as a long barrier for merging traffic. This can lead to merging failure and traffic performance degradation without proper treatment. This paper addresses the merging coordination problem for Connected and Automated Vehicles (CAVs) and Platoons of CAVs to achieve an efficient traffic flow at the merging zone without collisions. We present a bilevel framework where we decouple traffic coordination from vehicle motion control. At the traffic coordination level, a centralized coordinator schedules a merging time and speed for each approaching CAV passing through the merging point with Mixed Integer Linear Programming (MILP). The goal of the coordinator is to optimize traffic performance while considering the presence of platoons. At the vehicle control level, each vehicle plans its motion with the assigned schedule as terminal constraints. The individual motion plan is then followed by the vehicle while keeping a minimum safety distance to its neighbor. The resulting solution is evaluated in simulation and it is shown that our coordination framework can adequately manage traffic for the on-ramp merging scenario with CAVs and platoons.

The insertion of communication networks in feedback control loops complicates analysis and synthesis of cyber-physical systems (CPSs), and network-induced uncertainties may degrade system control performance. Thus, this book researches networked delay compensation and event-triggered control approaches for a series of CPSs subject to network-induced uncertainties. The authors begin with an introduction to the concepts and challenges of CPSs, followed by an overview of networked control approaches and event-triggered control strategies in CPSs. Then, networked delay compensation and event-triggered control approaches are proposed for CPSs with network communication delay, data dropout, signal quantization, and event-triggered communication. More specifically, networked delay compensation approaches are proposed for linear/nonlinear networked controlled plants with time-varying and random network communication delays and data dropouts. To reduce computational burden and network communication loads in CPSs, event-triggered control, self-triggered control, co-design of event-triggered control and quantized control techniques, and event-triggered disturbance rejection control approaches are also presented. This book is an essential text for researchers and engineers interested in cybersecurity, networked control, and CPSs. It would also prove useful for graduate students in the fields of science, engineering, and computer science.

Cooperative driving is an essential component of intelligent transport systems (ITSs). It promises greater safety, reduced accidents, efficient traffic flow, and fuel consumption reduction. Vehicle platooning is a representative service model for ITS. The principal sub-systems of platooning systems for connected and automated vehicles (CAVs) are cooperative adaptive cruise control (CACC) systems and platoon management systems. Based on vehicle state information received through vehicle-to-vehicle (V2V) communication, the CACC system allows platoon vehicles to maintain a narrower safety distance. In addition, the platoon management system using V2V communications allows vehicles to perform platoon maneuvers reliably and accurately. In this paper, we propose a CACC system with a variable time headway and a decentralized platoon join-in-middle maneuver protocol with a trajectory planning system considering the V2V communication delay for CAVs. The platoon join-in-middle maneuver is a challenging research subject as the research must consider the requirement of a more precise management protocol and lateral control for platoon safety and string stability. These CACC systems and protocols are implemented on a simulator for a connected and automated vehicle system, PreScan, and we validated our approach using a realistic control system and V2V communication system provided by PreScan.

In this letter, we consider a transportation network with a 100% penetration rate of connected and automated vehicles (CAVs) and present an optimal routing approach that takes into account the efficiency achieved in the network by coordinating the CAVs at specific traffic scenarios, e.g., intersections, merging roadways, and roundabouts. To derive the optimal route of a travel request, we use the information of the CAVs that have already received a routing solution. This enables each CAV to consider the traffic conditions on the roads. The solution of any new travel request determines the optimal travel time at each traffic scenario while satisfying all state, control, and safety constraints. We validate the performance of our framework through numerical simulations. To the best of our knowledge, this is the first attempt to consider the coordination of CAVs in a routing problem.

In this paper, an event-triggered vehicle-following control scheme for connected and automated vehicles (CAVs) is proposed considering nonideal Vehicle-to- Vehicle communications such as communication delays and packet dropouts. An output-based event-triggered mechanism is employed for reducing computational burden. An Event-Triggered Model Predictive Control (ETMPC) is proposed by combining with a multi-target controller for the lateral and longitudinal vehicle-following control of CAVs. The simulation results demonstrate that the proposed ETMPC can avoid unnecessary optimization implementation, achieving a computational reduction by 61.5% while maintaining the tracking precision compared with a conventional Model Predictive Controller. The proposed control scheme is also capable of being employed in vehicle platoon control.

Mixed platoon control of automated and human-driven vehicles at a signalized intersection: Dynamical analysis and optimal control

The development of connected and automated vehicle (CAV) techniques brings an upcoming revolution to traffic management. The control of CAVs in potential conflict areas such as on-ramps and intersections will be complex to traffic management when considering their deployment. There is still a lack of a general framework for dispatching CAVs in these bottlenecks, which is expected to ensure safety, traffic efficiency, and energy consumption in real time. This study aimed to fill the technique gap, and a comprehensive cooperative intelligent driving framework is put forward to study the problem, which can be used in both on-ramp and intersection scenarios. Based on a multi-objective evolutionary algorithm, CAVs are denoted as a sequence to be searched in solution space, while a multitask learning neural network with adaptive loss function is implemented for optimization target feedback to surrogate the simulation test procedure. The simulation results show that the proposed framework can get satisfying performance with low time and energy consumption. It can reduce time consumption by up to 16.51% for the on-ramp scenario and 9.8% for the intersection scenario, while reducing energy consumption by up to 16.39% and 11.39% for the two scenarios. Meanwhile, an analysis of computation time is carried out, illuminating the flexibility and controllability of the new strategy.