State Estimation of Multi-Agent Vehicle-Road Interaction Systems E-Book

Yan Wang

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 443

Veröffentlichungsjahr: 2025

Beschreibung

Up-to-date discussions of the challenges and solutions in state estimation of vehicle neighborhood systems

In State Estimation of Multi-Agent Vehicle-Road Interaction Systems, a team of distinguished researchers introduces a novel conceptual framework that defines a system comprising vehicles and local road segments within a connected vehicle (V2X) environment—referred to as the vehicle neighborhood system. Creative estimation methods for both states and parameters within this system have been proposed and potential applications of these methods have been discussed. The book places particular emphasis on estimating and analyzing the motion states of the ego vehicle and the preceding vehicle, as well as the tire road friction coefficient.

The book covers a wide range of topics in the area of vehicle neighborhood systems, including sensor technologies, data fusion, filtering algorithms, engineering applications, and practical implementations of autonomous driving systems. It also explores common challenges in state and parameter estimation for related nonlinear systems, such as sensor data loss, unknown measurement noise, and model parameter perturbations. Corresponding solutions to these issues are proposed and discussed in detail.

The book also includes:

• A thorough introduction to ego-vehicle state estimation with sensor data loss
• Comprehensive explorations of unknown noise and parameter perturbations in ego-vehicle state estimation
• Practical discussions of tire-road friction coefficient estimation with parameter mismatch and data loss
• Complete treatments of preceding vehicle state estimation

Perfect for engineers and professionals with an interest in vehicle state estimation, State Estimation of Multi-Agent Vehicle-Road Interaction Systems will also benefit academics, scientists, and graduate students in areas like robotics, control systems, and autonomous systems.

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Editor‐in‐Chief

Moeness Amin

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Ekram Hossain

Brian Johnson

Hai Li

James Lyke

Joydeep Mitra

Albert Wang

Desineni Subbaram Naidu

Yi Qian

Tony Quek

Behzad Razavi

Thomas Robertazzi

Patrick Chik Yue

State Estimation of Multi‐Agent Vehicle‐Road Interaction Systems

Yan WangThe Hong Kong Polytechnic University, Hong Kong

Guodong YinSoutheast University, China

Chao HuangThe University of Adelaide, Adelaide, South Australia, Australia

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About the Authors

Yan Wang, PhD, is a Research Fellow at The Hong Kong Polytechnic University, Hong Kong. His current research interests include vehicle system dynamics and automotive active safety control, where he contributed over 50 papers and obtained over 10 patents. He is also an Associate Editor for the Chinese Journal of Mechanical Engineering, Metrology and Measurement Systems, IEEE Open Journal of Signal Processing, and an Editorial board member of the Journal of Zhejiang University Science A, Measurement Science Review. Dr. Wang was the recipient of the Best Paper of IEEE ICUS in 2024, the Distinguished Youth Paper of Chinese Journal of Mechanical Engineering in 2024, and the Excellent Doctoral Dissertation Award of Jiangsu Province in 2023.

Guodong Yin, PhD, is a Professor at the School of Mechanical Engineering, Southeast University, Nanjing, China. His current research interests include vehicle dynamics control and connected vehicles, where he has contributed three book chapters, over 200 papers, and obtained 70 patents. He is an Associate Editor for the IEEE Transactions on Intelligent Vehicles, Journal of Intelligent and Connected Vehicles, and an Editorial Board for the Chinese Journal of Mechanical Engineering.

Chao Huang, PhD, is a Senior Lecturer at The University of Adelaide, Adelaide, South Australia, Australia. Her research interests include human‐machine collaboration, fault‐tolerant control, mobile robot (EV, UAV), and path planning and control, where she has contributed two book chapters, and over 100 papers. She is an Associate Editor for the IEEE Transactions on Transportation Electrification and IEEE Transactions on Intelligent Vehicles, IEEE Transactions on Consumer Electronics, Engineering Applications of Artificial Intelligence, International Journal of Control, Automation and Systems, and IEEE Women in Engineering Magazine.

Preface

Globally, the automotive industry is undergoing a profound transformation. The transition from traditional vehicles to intelligent vehicles, and from human driving to autonomous driving, is fundamentally reliant on perception technologies that capture information about vehicles and roads. Information such as vehicle state and tire‐road friction coefficient parameters is not only crucial for vehicle dynamics and control but also serves as core technologies in intelligent transportation systems and autonomous driving. With technological advancements, vehicle state estimation and tire‐road friction coefficient identification have gradually transitioned from theoretical research to practical applications. This journey includes significant milestones such as estimating the motion state and tire‐road friction coefficient of the host vehicle using onboard sensors and predicting the state of surrounding vehicles through vehicular communication networks. Additionally, the development of deep learning technologies has provided new solutions for acquiring this information.

To provide a unified description of the states of various traffic elements, the authors have proposed the concept of the multi‐agent vehicle‐road interaction system (MVRIS). This concept describes a system composed of a vehicle and its surrounding traffic elements, distinguishing it from traditional macro‐traffic research. In our book, the MVRIS consists of three main traffic elements: the host vehicle, the preceding vehicle, and the road. The authors have compiled their latest research work in this area into this book. The main topics discussed regarding the state estimation of the MVRIS include:

Ego‐vehicle state estimation considering sensor data loss.

Ego‐vehicle state estimation with unknown noise and parameter perturbations

State estimation of the preceding vehicle with data loss and parameter perturbations

Tire‐Road Friction Coefficient Estimation with parameters mismatch and data loss

With the development of 5G communication technology, edge computing, and cloud computing, MVRIS state estimation technology is set to play a crucial role in a broader range of applications. In the future, vehicles will not merely be means of transportation but will serve as intelligent mobile terminals and centers for data collection and processing. This evolution places higher demands and challenges on MVRIS state estimation technology. We believe that, with continuous technological advancements, the estimation of MVRIS states will play an increasingly important role in intelligent transportation and autonomous driving.

Professionals in the field of autonomous vehicles, as well as researchers, engineers, and scientists in related fields, can utilize State Estimation of Multi‐Agent Vehicle‐Road Interaction Systems to gain relevant knowledge. This book offers practical, precise, and validated algorithms that can be deployed in various real‐world scenarios.

Yan WangGuodong YinChao Huang

1Introduction

1.1 The Definition of Vehicle–Road Interaction System

Traffic accidents are one of the main causes of human casualties [1]. Intelligent connected vehicles will provide a new possibility for the automotive industry to effectively solve safety and congestion problems due to their functions of intelligent decision‐making and collaborative control. Some typical technologies include vehicle road coordination systems, advanced driver assistance systems (ADAS), etc. Some of the most representative technologies in ADAS include stability control systems [2, 3], braking control systems [4–7], local path planning systems [8, 9], active suspension control systems [10–12], etc. The prerequisite for these active safety systems to work effectively is to obtain accurate vehicle state and tire‐road friction coefficient (TRFC) [1]. To describe these vehicle states and road surface information in a unified way, this book adopts the concept of “interaction system” and defines the set composed of the host vehicle, the preceding vehicle, and the current road as the vehicle–road interaction system. As shown in Fig. 1.1, the corresponding variables, such as vehicle sideslip angle, tire stiffness, and TRFC, constitute the key state parameters in the vehicle–road interaction system. However, onboard sensors fail to directly obtain this information. Therefore, estimating these states using only onboard sensors is a hot topic of current research.

In the context of vehicle‐to‐vehicle (V2V) communication, the host vehicle, the preceding vehicle, and the road form a multi‐agent vehicle–road interaction system due to their dynamic interdependence and interactive roles. The host vehicle acts as an independent agent, constantly interacting with its environment by adjusting its control actions based on its own dynamic states and real‐time information from the preceding vehicle and road conditions. The preceding vehicle, another independent agent, influences the host vehicle's behavior through its speed, acceleration, and position, impacting car‐following decisions and safety measures. Additionally, the road, though not a vehicle, can be viewed as an agent due to its influence on vehicle dynamics via tire–road interactions, such as road friction and surface conditions, which directly affect vehicle stability and performance. These three components—host vehicle, preceding vehicle, and road—communicate and interact within a shared environment, forming a tightly coupled multi‐agent system. This framework allows for more accurate state estimation and decision‐making, which is essential for the development of ADAS and autonomous driving technologies. In previous studies, host vehicle and preceding vehicle state estimation, as well as TRFC identification, are usually considered as two types of parameter identification problems. However, in this book, we try to define a new concept to describe a more macroscopic vehicle–road coupled system. This will provide a new perspective to researchers in this field. Therefore, these different states, or TRFC, will become the internal states of this macroscopic system.

Figure 1.1 The vehicle–road interaction system.

1.2 The Importance of State Estimation for Vehicle–Road Interaction System

State estimation plays a crucial role in the development of vehicle–road interaction systems, directly impacting the safety, efficiency, and reliability of vehicles. This technology is fundamental for the operation of autonomous vehicles, as it allows for accurate sensing and understanding of both the vehicle's state and its surrounding environment. By doing so, state estimation enables intelligent vehicles to make autonomous decisions and execute complex driving maneuvers. Accurate state estimation supports ADAS such as adaptive cruise control (ACC), lane‐keeping assist, and emergency braking. For ACC, state estimation helps maintain a safe distance from the vehicle ahead by continuously monitoring relative speed and distance. Lane‐keeping assist relies on state estimation to ensure the vehicle stays centered in its lane by detecting lane markings and making necessary steering adjustments. Emergency braking systems use state estimation to detect potential collisions and apply brakes in time to avoid or mitigate the impact. Furthermore, state estimation enhances the efficiency and reliability of autonomous vehicles. By optimizing driving patterns based on accurate state information, vehicles can achieve smoother acceleration and braking, better fuel economy, and reduced emissions. Reliable state estimation ensures that autonomous vehicles can operate consistently in various conditions, from clear weather to rain or snow, thus building trust in autonomous vehicle technology. In summary, vehicle state estimation is a cornerstone of autonomous vehicle technology. It integrates sensor data, mathematical models, and advanced algorithms to provide a comprehensive understanding of a vehicle's dynamics and its immediate environment. This enables intelligent vehicles to make informed, autonomous decisions, ultimately improving the safety, efficiency, and reliability of modern transportation systems.

Vehicle state estimation involves the real‐time determination of a vehicle's yaw rate, sideslip angle, velocity, and other pertinent parameters. These parameters are critical for assessing the vehicle's current status and predicting its future behavior, which is essential for facilitating safe and effective decision‐making in autonomous vehicles.

The yaw rate of a vehicle, representing its rotational motion around the vertical axis, holds a central position in the realm of vehicle dynamics. It directly impacts the vehicle's stability during maneuvers such as turns and lane changes. A controlled and well‐monitored yaw rate is critical for preventing oversteer or understeer conditions, both of which can lead to loss of control and compromise safety. In the context of vehicle dynamics, yaw rate plays a pivotal role in ensuring the vehicle's stability and responsiveness. During a turn, the yaw rate determines how quickly the vehicle rotates about its vertical axis. If the yaw rate is too high, it can result in oversteer, where the rear wheels lose traction and the vehicle turns more sharply than intended. Conversely, if the yaw rate is too low, it can lead to understeer, where the front wheels lose traction, causing the vehicle to turn less sharply than the driver intends. Both conditions can be dangerous, especially at high speeds or on slippery surfaces. By precisely managing the yaw rate, vehicles can navigate corners with optimal stability, reducing the risk of skidding or rollovers. This is achieved through advanced control systems such as electronic stability control (ESC), which continuously monitors the yaw rate and other parameters to make real‐time adjustments. ESC systems apply brake force to individual wheels and adjust engine power to correct oversteer or understeer, helping the driver maintain control of the vehicle. The importance of yaw rate control becomes even more evident in emergency situations. Rapid changes in direction, such as during evasive maneuvers to avoid an obstacle, demand judicious control of the yaw rate to ensure the vehicle's response aligns with the driver's intentions. In such scenarios, the ability to swiftly and accurately adjust the yaw rate can make the difference between avoiding a collision and losing control. For instance, consider a situation where a driver must swerve to avoid a sudden obstacle on the road. The vehicle's stability control system, relying on yaw rate sensors, will detect the rapid change in direction and intervene to maintain stability. By modulating brake pressure on individual wheels and adjusting throttle input, the system helps the vehicle follow the desired path while preventing oversteer or understeer. This intervention occurs in a matter of milliseconds, often faster than a human driver can react, thereby enhancing safety. Moreover, maintaining an optimal yaw rate is crucial for ensuring passenger comfort. Sudden or excessive rotational movements can be unsettling for passengers, leading to discomfort and motion sickness. By managing the yaw rate effectively, the vehicle can provide a smoother ride, enhancing overall comfort and driving experience. In the realm of autonomous driving, yaw rate control is even more critical. Autonomous vehicles rely on precise control of all dynamic parameters, including yaw rate, to execute complex maneuvers safely and efficiently. Advanced algorithms and sensor fusion techniques are employed to continuously monitor and adjust the yaw rate, ensuring the vehicle remains stable and responsive under all conditions.

In conclusion, the yaw rate of a vehicle is a fundamental aspect of vehicle dynamics, and is crucial for maintaining stability and safety during various driving maneuvers. Whether it is preventing oversteer and understeer in everyday driving or ensuring precise control during emergency situations, effective yaw rate management is essential. Advanced stability control systems and autonomous driving technologies rely heavily on yaw rate data to enhance vehicle performance and passenger safety, underscoring its significance in modern automotive engineering.

The sideslip angle, indicating the angle between a vehicle's velocity vector and its heading angle, is a fundamental parameter influencing lateral stability. A controlled sideslip angle is integral to preventing uncontrollable skidding and maintaining the vehicle's trajectory during dynamic maneuvers. This parameter is critical for ensuring that the vehicle responds predictably to driver inputs, particularly during high‐speed driving, abrupt steering inputs, or when navigating adverse road conditions. In the context of vehicle dynamics, the sideslip angle plays a pivotal role in maintaining the vehicle's stability. When a vehicle is in motion, its tires generate lateral forces to counteract any sideways motion. The sideslip angle quantifies the deviation between the vehicle's intended path and its actual path. If this angle becomes too large, it indicates that the tires are losing grip on the road surface, which can lead to a loss of control and potential skidding. Therefore, managing the sideslip angle is crucial for maintaining the vehicle's lateral stability and ensuring safe handling characteristics. Excessive sideslip angles can lead to loss of tire grip, compromising the vehicle's ability to respond predictably to driver commands. For instance, during a sharp turn or a sudden evasive maneuver, the sideslip angle increases as the lateral forces acting on the tires intensify. If the tires exceed their grip limit, they will start to slide sideways, resulting in a loss of control. This situation is particularly dangerous on slippery or uneven road surfaces, where the risk of skidding is higher. By controlling the sideslip angle, vehicles can maintain optimal tire grip, ensuring stable and predictable handling.

Advanced control systems play a crucial role in managing the sideslip angle to enhance vehicle safety. Modern vehicles are equipped with sophisticated systems such as ESC and traction control systems (TCS) that continuously monitor and adjust the sideslip angle. These systems use sensors to measure the vehicle's speed, steering angle, and yaw rate, among other parameters. By analyzing this data in real time, they can detect any deviation from the intended path and apply corrective measures. For example, if the ESC system detects that the sideslip angle is increasing beyond safe limits during a turn, it can selectively apply brake force to individual wheels and adjust engine power to counteract the sideways motion. This helps to bring the vehicle back on its intended trajectory, reducing the risk of skidding and enhancing overall stability. Similarly, the TCS system can modulate the power delivery to the wheels to prevent excessive wheel spin and maintain optimal traction, especially on slippery surfaces. The importance of controlling the sideslip angle is particularly evident in emergency situations. During sudden maneuvers to avoid obstacles or navigate sharp curves, the sideslip angle can change rapidly. Advanced control systems must react swiftly to these changes to maintain vehicle stability. By keeping the sideslip angle within stable operational limits, these systems enhance the vehicle's ability to respond effectively to driver commands, ensuring a safer driving experience. In autonomous vehicles, the management of the sideslip angle is even more critical. Autonomous driving algorithms rely on precise control of all vehicle dynamics to execute complex maneuvers safely. These algorithms use advanced sensor fusion techniques and predictive models to monitor and adjust the sideslip angle continuously. This ensures that the autonomous vehicle can navigate through various driving conditions with optimal stability and safety.

In conclusion, the sideslip angle is a vital parameter influencing the lateral stability of a vehicle. Effective management of the sideslip angle is essential to prevent uncontrollable skidding, maintain the vehicle's trajectory, and ensure predictable handling. Advanced control systems such as ESC and TCS are crucial in continuously monitoring and adjusting the sideslip angle to enhance vehicle safety. By maintaining stable operational limits, these systems contribute significantly to overall road safety, providing a safer and more reliable driving experience.

Longitudinal velocity, representing the rate of change of a vehicle's position along its direction of motion, and lateral velocity, depicting the rate of change of position perpendicular to the direction of motion, collectively play pivotal roles in determining a vehicle's stability, maneuverability, and response to various driving conditions. These two components of velocity are integral to understanding and managing a vehicle's dynamics. Longitudinal velocity, often associated with acceleration and deceleration, directly affects a vehicle's dynamics and braking performance. In emergency braking situations, the ability to modulate longitudinal velocity is crucial for avoiding collisions and ensuring the safety of occupants and pedestrians. Advanced antilock braking systems (ABS) and ESC mechanisms leverage longitudinal velocity data to optimize braking forces, preventing wheel lockup and skidding. ABS prevents the wheels from locking up during hard braking, allowing the driver to maintain steering control. ESC, on the other hand, helps to maintain vehicle stability by detecting and reducing the loss of traction. By continuously monitoring longitudinal velocity, these systems can adjust braking force distribution to ensure maximum efficiency and safety. Longitudinal velocity is also integral to the operation of ACC systems. ACC systems maintain a set following distance from the vehicle ahead by adjusting the throttle and brake based on longitudinal velocity. This enhances safety by providing a seamless response to changes in traffic conditions, reducing the risk of rear‐end collisions. The ACC system uses sensors to monitor the speed and distance of the vehicle in front, adjusting the vehicle's speed accordingly. This not only ensures a safer driving experience but also enhances comfort by reducing the need for manual speed adjustments in varying traffic conditions. Moreover, collision avoidance systems utilize longitudinal velocity information to assess the risk of an impending collision and initiate pre‐crash measures, such as autonomous emergency braking (AEB). AEB systems are designed to detect potential collisions and automatically apply the brakes if the driver does not respond in time. By analyzing longitudinal velocity along with other parameters like the distance to the obstacle and the relative speed, these systems can determine the likelihood of a collision and take preventive action. This significantly reduces the chances of accidents, protecting both the vehicle's occupants and other road users.

Lateral velocity, while less commonly discussed, is equally important for vehicle stability and maneuverability. Lateral velocity affects how the vehicle responds to steering inputs and how well it can maintain its intended path, especially during cornering or lane changes. High lateral velocities can lead to oversteer or understeer, where the vehicle either turns more sharply or less sharply than intended. Effective control of lateral velocity is essential for maintaining stability and preventing accidents, particularly in high‐speed driving or adverse weather conditions. Advanced vehicle dynamics control systems, such as ESC and TCS, monitor and adjust both longitudinal and lateral velocities to enhance stability and safety. These systems use a network of sensors to gather real‐time data on the vehicle's motion and the road conditions. By analyzing this data, they can make precise adjustments to the braking force, throttle, and steering inputs to maintain optimal stability. For instance, if the vehicle begins to oversteer, ESC can reduce engine power and apply braking to individual wheels to help regain control. Similarly, TCS can prevent wheel spin during acceleration by adjusting the throttle and brake. The integration of longitudinal and lateral velocity data is crucial for the development of autonomous driving technologies. Autonomous vehicles rely on accurate and continuous monitoring of these parameters to navigate safely and efficiently. Advanced algorithms and machine learning techniques are used to process the velocity data and make real‐time decisions. For example, when navigating a sharp turn, the autonomous system must balance both longitudinal and lateral velocities to ensure a smooth and safe maneuver. This involves adjusting the speed and steering angle precisely to maintain stability and adhere to the intended path.

In summary, longitudinal and lateral velocities are fundamental to vehicle dynamics, playing critical roles in ensuring stability, maneuverability, and safety. Longitudinal velocity is crucial for acceleration, deceleration, and braking performance, impacting systems such as ABS, ESC, ACC, and collision avoidance. Lateral velocity, on the other hand, influences how well the vehicle maintains its path and responds to steering inputs. Advanced control systems continuously monitor and adjust both velocities to enhance overall vehicle performance and safety. As the automotive industry advances towards greater automation, the precise control and integration of longitudinal and lateral velocity data will be essential for developing safe and reliable autonomous vehicles.

As tires are the only components connecting the vehicle to the ground, motion control or stability control of vehicles ultimately translates into the control of motor torque and braking torque. The TRFC directly limits the maximum tire forces available for the vehicle. Furthermore, many ADAS or high‐level autonomous vehicles require dynamic adjustments in longitudinal and lateral control to enhance vehicle safety based on the TRFC. Understanding and accurately assessing the TRFC are crucial for optimizing the performance and safety of these systems. The main function of ABS is to prevent the wheels from locking during heavy braking and to maintain the traction between the tires and the road at an optimal value. The magnitude of this optimal traction is usually determined based on the TRFC. ABS works by modulating the brake pressure to prevent wheel lockup, thereby maintaining steerability and stability during braking. When the TRFC is high, ABS can allow for more aggressive braking without the risk of wheel lockup. Conversely, when the TRFC is low, such as on icy or wet roads, the ABS adjusts to provide gentler braking to maintain control. ESC systems generate a yaw moment based on the desired yaw rate to ensure the lateral stability of the vehicle. The desired yaw rate normally shows a positive correlation with the TRFC. By continuously monitoring the TRFC, ESC systems can adjust the braking force applied to individual wheels to correct understeer or oversteer conditions. For instance, if the vehicle begins to oversteer, the ESC system can apply the brake to the outer front wheel, generating a counteracting force that helps stabilize the vehicle. Accurate TRFC information allows the ESC system to make precise adjustments, enhancing the vehicle's ability to maintain its intended path, especially in challenging driving conditions. Active collision‐avoidance systems use a variety of sensors to obtain information about the surrounding environment of the vehicle to reduce the risk of accidents. These systems work by assessing the relative distance between the vehicle and potential obstacles and initiating preemptive actions when this distance falls below a safety threshold. This safety distance is negatively correlated with the TRFC. In other words, when the TRFC is low, the safety distance must be increased to account for the reduced traction and longer stopping distances. Conversely, when the TRFC is high, the vehicle can safely operate with a shorter safety distance. By integrating TRFC data, collision avoidance systems can more accurately determine when to initiate braking or evasive maneuvers, thereby reducing the likelihood of collisions. Additionally, TRFC plays a crucial role in the operation of ACC systems. ACC systems maintain a set following distance from the vehicle ahead by adjusting the throttle and brake based on the longitudinal velocity of the vehicle. When the TRFC is high, the ACC system can operate more aggressively, allowing for closer following distances and more responsive acceleration and deceleration. However, when the TRFC is low, the ACC system must adjust to maintain a greater following distance and smoother speed changes to ensure safety. The accurate assessment of TRFC is also essential for the performance of high‐level autonomous vehicles. Autonomous driving algorithms rely on precise TRFC data to make real‐time decisions about acceleration, braking, and steering. For example, when navigating a sharp turn, the autonomous system must balance both longitudinal and lateral forces to maintain stability. Accurate TRFC information allows the system to adjust the speed and steering angle precisely, ensuring a smooth and safe maneuver.

Moreover, ADAS such as TCS also depend on accurate TRFC data. TCS works by preventing wheel spin during acceleration by adjusting the throttle and brake. When the TRFC is high, TCS can allow for more aggressive acceleration without the risk of wheel spin. Conversely, when the TRFC is low, TCS must apply more conservative throttle control to maintain traction. By continuously monitoring and adjusting based on TRFC, TCS enhances the vehicle's ability to accelerate safely in various road conditions.

In summary, the TRFC is a critical parameter for vehicle dynamics and safety systems. It directly influences the performance of ABS, ESC, active collision avoidance, ACC, and TCS, among others. Accurate TRFC information allows these systems to make precise adjustments, enhancing vehicle stability, maneuverability, and safety. As the automotive industry continues to advance towards greater automation and improved safety features, the importance of accurate TRFC assessment will only grow. Ensuring that active safety systems have reliable TRFC data is essential for optimizing their performance and ultimately contributing to safer roads.

Although the yaw rate, sideslip angle, velocity of a vehicle, and TRFC are crucial dynamic parameters that significantly influence its handling and stability, these parameters cannot be directly measured and require estimation methods for determination. Directly measuring the yaw rate of a vehicle is challenging, as it represents the rotational speed around the vertical axis. Conventional vehicle sensors typically do not provide this precise information, necessitating estimation through alternative measured data and models. Direct measurement of the sideslip angle often requires specialized sensors, such as an inertial navigation system. However, in the case of most conventional vehicles, there is no sensor configuration designed for the direct measurement of the sideslip angle. Traditional vehicle sensors primarily focus on parameters like speed, angular velocity, and acceleration, lacking a dedicated sensor for sideslip angle measurement. Consequently, direct measurement of the sideslip angle is often impractical in many situations due to the absence of specific sensors. For longitudinal velocity, while some vehicles are equipped with speed‐measuring sensors such as wheel speed sensors or GPS systems, there are situations where the measurements from these sensors may not be accurate or available. For lateral velocity, traditional vehicle sensors are effective for certain dynamic measurements; they do not provide a direct measurement of lateral velocity. Specific sensors designed solely for the direct measurement of lateral velocity are not commonly integrated into standard vehicle sensor setups. The absence of dedicated lateral velocity sensors limits the availability of direct measurement options. Similarly, TRFC cannot be measured by onboard sensors.

The process of vehicle state estimation relies on an intricate interplay between sensor data, mathematical models, and sophisticated algorithms. Sensors such as GPS, LiDAR, radar, and cameras provide raw data about the vehicle's position, speed, and surroundings. This sensor data is then processed using mathematical models that describe the vehicle's dynamics, including its mass, center of gravity, and aerodynamic properties. These models help in predicting how the vehicle will respond to different inputs, such as steering, acceleration, and braking. Sophisticated algorithms, such as Kalman filters, particle filters, and machine learning techniques, are employed to fuse the sensor data and refine the state estimates. Kalman filters, for example, are used to recursively estimate the state of the vehicle by combining predictions from the mathematical models with real‐time sensor data. To this end, researchers have successively proposed various estimation approaches to address the challenge. Based on the above discussion, some key importance for vehicle–road interaction systems can be summarized.

1.2.1 Enhancing Safety

State estimation is one of the core technologies for ensuring the safe operation of intelligent vehicles. With accurate state estimation, vehicles can perceive their position, speed, and direction in real time, as well as detect other objects in the environment, such as pedestrians, other vehicles, and obstacles. This perception capability allows intelligent vehicles to react promptly and avoid collisions and other potential hazards. For instance, if an obstacle suddenly appears in front of the vehicle, state estimation can quickly identify and convey the information to the control system, enabling necessary evasive or emergency braking maneuvers.

1.2.2 Improving Driving Efficiency

Through state estimation, intelligent vehicles can optimize their driving paths and behaviors, thus improving overall driving efficiency. By monitoring vehicle states and traffic conditions in real time, intelligent vehicles can choose the optimal path, avoid congestion, and adjust speed and trajectory based on traffic signals and the behavior of other vehicles. This not only reduces travel time but also lowers fuel consumption and emissions, achieving more environmentally friendly travel.

1.2.3 Enhancing Autonomous Decision‐making Capabilities

State estimation empowers intelligent vehicles with autonomous decision‐making capabilities, allowing them to make independent judgments in complex and variable traffic environments. For example, when faced with changing traffic signals, pedestrian crossings, and emergency vehicle priority, intelligent vehicles need to quickly assess the current state and make corresponding decisions. Through accurate state estimation, vehicles can promptly obtain necessary information, perform risk assessments, and execute decisions to ensure a safe and efficient driving experience.

1.2.4 Supporting ADAS

State estimation is the foundation for realizing ADAS. ADAS features, such as ACC, lane‐keeping assist, and automated parking, rely on accurate state estimation to perceive the vehicle and environmental states. These systems provide necessary driving assistance through real‐time monitoring and data analysis, reducing driver burden and enhancing driving safety. For instance, ACC systems can accurately measure the distance and speed of the vehicle ahead through state estimation, adjusting their speed to maintain a safe following distance.

1.2.5 The Foundation of Future Traffic Systems

As technology advances, state estimation's role in future traffic systems will become more prominent. The development of intelligent transportation systems (ITS) relies on efficient communication and coordination between vehicles and between vehicles and infrastructure, and state estimation provides the foundation for achieving this goal. By sharing and integrating various state data, ITS can achieve more efficient traffic management, reduce congestion and accidents, and improve overall traffic flow.

1.2.6 Enhancing User Experience

State estimation not only improves the technical performance of intelligent vehicles but also significantly enhances the user experience. Through precise navigation and smooth driving behavior, passengers can enjoy a more comfortable ride. Additionally, various autonomous driving and driver assistance functions supported by state estimation make driving easier and safer, increasing user trust and satisfaction with intelligent vehicles.

In conclusion, the importance of state estimation for intelligent vehicles is undeniable. It is the key technology ensuring safe driving, precise navigation, improved driving efficiency, enhanced autonomous decision‐making capabilities, and adaptation to complex environments. As intelligent vehicle technology continues to develop, state estimation will continue to play a central role in advancing autonomous driving and ITS. By continuously improving the accuracy and reliability of state estimation, we can look forward to a future with safer, more efficient, and more comfortable travel methods.