• Modular legal-interpretation layer with hybrid vector–graph RAG for AV guidance. • Two datasets: SFT and RAG for multilingual, cross-jurisdiction evaluation. • Hybrid retrieval improves faithfulness and reduces hallucination vs single modes. • Trust module scores context, groundedness, and answer relevance online. • Validated in smart-cabin, V2X intersection monitoring, and offline auditing. Autonomous vehicles (AVs) face persistent challenges in complying with complex and evolving traffic laws. Existing approaches, including rule-based, learning-based, and large language model (LLM) methods, each face limits in adaptability, generalizability, or trustworthiness. We present DriveLegal , a modular legal-interpretation framework for downstream autonomous driving applications. DriveLegal pairs fine-tuned multilingual large language models (LLMs) with an intelligent hybrid retrieval module that routes between vector search and knowledge graph, then returns concise, cited answers. A trust layer scores context relevance, groundedness, and answer relevance and supports continuous improvement through periodic automatic signals and targeted human review. We introduce the DriveLegal datasets for supervised fine-tuning and for retrieval and graph reasoning. Across benchmarks and case studies in smart cabin and vehicle-to-everything (V2X) settings, the hybrid retrieval strategy improves contextual accuracy and reduces hallucination while producing jurisdiction-aware outputs suitable for compliance checks, incident analysis, and reporting.

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.

Learning-based algorithms play a pivotal role in various functional modules of an autonomous driving system. Recognizing and accounting for the impact of learning-based algorithm uncertainties on other functional modules can be crucial for making more dependable driving behavior decisions and for selecting more appropriate driving precaution measures, as opposed to directly executing safety fallback strategies like emergency braking. With the motivation of optimizing the safety without unnecessary disruption to the driving experience, this paper proposes an uncertainty-aware, dual-tiered decision making method named DBNID, which is based on dynamic Bayesian network (DBN) and influence diagram (ID). To begin, the paper formulates the effects of uncertainty propagation stemming from perception and prediction modules using a DBN model. The effects are then solved by an expectation maximum (EM) algorithm. Furthermore, how the uncertainty propagation effects are considered in the decision making process is then presented in an ID model with the introduction of the utility function formulation. Finally, the proposed DBNID method is evaluated on a simulation platform tailored for real-world autonomous driving testing. By considering uncertainty propagation, the results demonstrate that the proposed method can significantly reduce the likelihood of violating critical safe stop requirements, while simultaneously enhancing the minimum time-to-collision (TTC) performance. DBNID method offers valuable insights of integrating learning-based algorithm uncertainties into autonomous vehicle decision making process.

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.

Connected autonomous vehicle (CAV) systems could bring considerable benefits to our daily lives, and possibly outperform single autonomous vehicle (AV). Nevertheless, the real-time determination of the optimal route for each connected autonomous vehicle (CAV) within a continuous space presents a considerable challenge. This difficulty arises from the exponential growth of potential motion combinations for CAVs, considering the diverse road geometries they encounter. This article proposed a CAV group planning framework to overcome this challenge. The framework works hierarchically. The global and local controllers play a crucial role in generating long-term reference paths for each CAV by employing a versatile road geometry model capable of accommodating diverse road shapes. Initially, waypoints are extracted utilizing this generalized road geometric model. Subsequently, potential combinations of waypoints are generated by considering the CAV group as a fleet. Finally, optimal waypoint combinations are assigned to each CAV by considering the CAVs’ own benefit and road usage. Reference paths for each CAV are generated using the selected waypoints and are passed on to the CAVs and roadside units (RSUs) layer. The CAVs and RSUs generate short-term motion, given the reference paths. This is operated in the Frenet frame, and the optimal motion for each CAV is selected in the aspect of the entire CAV fleet. The proposed framework is tested in simulation and has shown the ability to generate safe and sound paths under various road geometries with obstacles and in mixed traffics in real time.

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.

Perception systems play a crucial role in autonomous driving by reading the sensory data and providing meaningful interpretation of the operating environment for decision-making and planning. Guaranteeing a safe perception performance is the foundation for high-level autonomy, so that we can hand over the driving and monitoring tasks to the machine with ease. With the motivation of improving the perception systems’ safety, this survey analyzes and reviews the current achievements of safety-related standards and definitions, sensory modeling, and metrics for perception tasks in autonomous driving applications. Furthermore, it covers the generic categorization of potential failures and causal analysis in perception tasks, correlates the effect with the scenario modelling choices, and highlights major triumphs and noted limitations encountered by current research efforts. The new safety challenges laid out by the information exchange stage of the connected autonomous vehicle application have also been summarized. The open research questions and future directions are outlined to welcome researchers and practitioners to this exciting domain.

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.

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

Detecting driver inattentive behaviors is crucial for driving safety in a driver monitoring system (DMS). Recent works treat driver distraction detection as a multiclass action recognition problem or a binary anomaly detection problem. The former approach aims to classify a fixed set of action classes. Although specific distraction classes can be predicted, this approach is inflexible to detect unknown driver anomalies. The latter approach mixes all distraction actions into one class: anomalous driving. Because the objective focuses on finding the difference between safe and distracted driving, this approach has better generalization in detecting unknown driver distractions. However, a detailed classification of the distraction is missing from the predictions, meaning that the downstream DMS can only treat all distractions with the same severity. In this work, we propose a two-phase anomaly proposal and classification framework [driver anomaly detection and classification network (DADCNet)] robust for open-set anomalies while maintaining high-level distraction understanding. DADCNet makes efficient allocation of multimodal and multiview inputs. The anomaly proposal network first utilizes a subset of the available modalities and views to suggest suspicious anomalous driving behavior. Then, the classification network employs more features to verify the anomaly proposal and classify the proposed distraction action. Through extensive experiments in two driver distraction datasets, our approach significantly reduces the total amount of computation during inference time while maintaining high anomaly detection sensitivity and robust performance in classifying common driver distractions.

Accurate forecasting of future traffic is a critical contemporary problem for transportation research. However, it is difficult to understand the feature patterns of traffic events due to the complexity of the traffic environment, heterogeneous factors, and lack of abnormal samples. This article proposes a framework to integrate the social traffic data and use the TabNet model to facilitate the representation learning task in traffic event prediction. With the tabular learning and model interpretability analysis, the importance of common traffic external factors toward traffic events is studied. The study has practical significance for regulating traffic planning and the development of the operational boundary for autonomous driving systems.

Predicting object motion behaviour is a challenging but crucial task for safe decision making and path planning for autonomous vehicles. It is challenging in large part due to the uncertain, multi-modal, and practically intractable set of possible agent-agent and agent-space interactions, especially in urban driving settings. Models solely based on constant velocity or social force have an inherent bias and may lead to inaccurate predictions across the prediction horizon whereas purely data driven approaches suffer from a lack of holistic set of rules governing predictions. We tackle this problem by introducing MPC-PF: a novel potential field-based trajectory predictor that incorporates social interaction via agent-agent and agent-space considerations and is able to tradeoff between inherent model biases across the prediction horizon. Through evaluation on the Waymo Open Motion Dataset and a variety of other common urban driving scenarios, we show that our model is capable of achieving state-of-the-art performance while producing accurate predictions for both short and long term timesteps. We also demonstrate the significance of our model architecture through an ablation study.

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.

Variable-structure cable-driven parallel robots (VSCR) are a new class of cable robots that are able to cover nonconvex installation spaces by permitting collisions between cables and fixed objects in the environment. In this article, we show how the configuration space of a general planar VSCR can be represented as an organized set of partially overlapping regions of constant structure. The benefit of this representation, which we refer to as the “structure atlas,” is that it allows any techniques from the established cable-driven parallel robot literature to be applied locally, greatly simplifying the modeling complexity associated with VSCRs. A complete method for how such a representation can be constructed is provided, which includes identifying the set of reachable kinematic structures for a given VSCR and the area where each structure is active. We then give specific examples of how this new representation can be used for performing VSCR workspace analysis and directly solving the VSCR inverse kinematics problem. Our results are demonstrated with the aid of simulated and experimental results.

Intelligent tire systems are promising solutions for achieving precise vehicle state estimations, localization, and motion control in the context of autonomous driving. Tire cornering properties, namely, lateral force, aligning moment, and pneumatic trail, are crucial factors that should be accurately estimated for vehicle dynamics control purposes. In this work, a soft sensor for estimating tire cornering properties based on intelligent tire and machine learning is developed. The intelligent tire system is based on a triaxial accelerometer mounted on the inner liner of the tire tread, which provides acceleration measurements from the$x$,$y$, and$z$directions. Partial least squares and variable importance in the projection scores (PLS-VIP) are used in the feature extraction of the acceleration signals over the contact patch. A Gaussian process regression (GPR) model is trained to predict the cornering properties with confidence intervals under different input conditions. Based on the variances in the GPR predictions and minimum mean-square error criterion, a data fusion method for pneumatic trail estimation is proposed. It is demonstrated that the developed GPR models for cornering properties and the data fusion method for pneumatic trail estimation have satisfactory accuracy and reliability. The experimental results show that the soft sensor proposed in this work is a strong candidate for further applications in the development of vehicle state estimation and control algorithms.

Predicting object motion behaviour is a challenging but crucial task for safe decision making and path planning for an autonomous vehicle. It is challenging in large part due to the uncertain, multi-modal, and practically intractable set of possible agent-agent and agent-space interactions, especially in urban driving settings. Models solely based on constant velocity or social force have an inherent bias and may lead to inaccurate predictions across the prediction horizon whereas purely data driven approaches suffer from a lack of a holistic set of rules governing predictions. We tackle this problem by introducing MPC-PF: a novel potential field-based trajectory predictor that incorporates social interaction and is able to tradeoff between inherent model biases across the prediction horizon. Through evaluation on a variety of common urban driving scenarios, we show that our model is capable of producing accurate predictions for both short and long term timesteps. We also demonstrate the significance of our model architecture through an ablation study.

Vehicle-trailer systems have various unstable modes including trailer snaking, jack-knifing, and roll-over, which should be considered in their stability control. For stability control design purposes, various techniques have been proposed to estimate vehicle-trailer system states and parameters. Some of these techniques rely on vehicle kinematic/dynamic models while others are data-driven and do not require a model. This review paper provides a comprehensive overview of different model-based and non-model-based techniques/algorithms developed for estimating vehicle-trailer system states and parameters. The main features, limitations, and assumptions for each estimation method are discussed. The trailer parameter estimation feasibility is also investigated for different possible vehicle-trailer on-board sensor settings. This paper can be used as a review and reference resource for engineers working in vehicle with semi-trailer state estimation and safety systems.

In this paper, ultra-sonic sensors along with kinematics and dynamics equations of a towing vehicle are used to develop three approaches for hitch angle estimation. The first approach is based on direct calculation of hitch angle using certain a priori geometric information and distance measurements of four ultra-sonic sensors. An angle estimate is generated for each of the six possible sensor-pair combinations, and these six estimates are passed through a voting algorithm to produce a single estimate. As the second and third approaches, kinematic and dynamic models of the tractor-trailer system are used to develop least-squares and Kalman filtering based recursive hitch angle estimations. A more reliable hitch angle estimation scheme is then proposed as the integration of the algorithms developed following each of the three approaches via a switching data fusion logic. It is shown that the proposed integrated hitch angle estimation scheme can be used for any ball type box trailer with a flat or symmetric V-nose frontal face without any priori information on the trailer parameters. Based on the validity of the assumptions, the proposed scheme can estimate the hitch angle in both low-speed using the kinematic model, and high-speed using the dynamic model of the tractor-trailer system. Experimental results corroborate the algorithms in estimating the hitch angle estimates in various cases conducted in this study.

In the research of autonomous vehicles, most existing studies treat the decision/planning and control as two separate problems. This idea originates from robotics. But since there are essential differences between robot and autonomous vehicle, the structure in Robotics may not be suitable for autonomous vehicles. Considering decision/planning and control separately may affect the performance of autonomous vehicle under complex driving conditions. To fill in the research gap, this paper proposes a novel scheme which considers the local motion planning and control in a combined manner. Firstly, the local motion planning is transformed into the longitudinal control problem based on the proposed scenario adaptive MPC, by which the motion behavior (driving along the global path, car-following, lane-change) can be automatically decided. Then, the lateral MPC controller is designed to track the global path and conduct the local motion commands. To ensure the performance of the path tracking control and a smooth lane-change process simultaneously, an adaptive weight mechanism is introduced in the lateral controller. Comprehensive case studies including both straight and curve road are conducted based on Carsim-Simulink co-simulation platform. The results show that the proposed algorithm can not only ensure the vehicle safety in complex driving conditions, but also ensure that the vehicle can drive at its desired velocity as much as possible by intelligently judging the most proper motion behaviors.

The concept of intelligent tires has drawn the attention of researchers in the areas of autonomous driving, advanced vehicle control, and artificial intelligence. The focus of this paper is on intelligent tires and the application of machine learning techniques to tire force estimation. We present an intelligent tire system with a tri-axial acceleration sensor, which is installed onto the inner liner of the tire. Neural Network techniques are used for real-time processing of the sensor data. The accelerometer is capable of measuring the acceleration in x,y, and z directions. When the accelerometer enters the tire contact patch, it starts generating signals until it fully leaves it. Simultaneously, by using MTS Flat-Trac test platform, tire actual forces are measured. Signals generated by the accelerometer and MTS Flat-Trac testing system are used for training three different machine learning techniques with the purpose of online prediction of tire forces. It is shown that the developed intelligent tire in conjunction with machine learning is effective in accurate prediction of tire forces under different driving conditions. The results presented in this work will open a new avenue of research in the area of intelligent tires, vehicle systems, and tire force estimation.

Tire states and capacity monitoring is critical for vehicle and wheel stabilization controls in automated driving and active safety systems. Tire capacity, which represents the performance margin of tire forces from its limits, determines the operational range for vehicle control systems and their actuation through steering or torques at each tire to maintain stability while performing trajectory following. This paper presents a generic tire capacity identification framework that can handle different normal loads, road surface friction, and combined-slip driving scenarios, which are challenging for stabilization and tracking control programs in automated driving systems. A novel measuring method for generating force-training data is designed by combining the indoor tire test procedure and tread rubber friction test rig, in order to obtain adequate and high-quality benchmark datasets. The results from large data sets from road experimenting and indoor tire test facilities, including pure- and combined-slip conditions, confirm effectiveness of the developed learning-based tire capacity estimation which utilizes notions from the model description with bounded uncertainty. More importantly, the proposed method can provide reliable tire properties ranging from the linear to the sliding regions. Further validation is performed on a real test car with on-board sensory measurements, and the results confirm accuracy of the proposed method for various free rolling and hard launch/brake scenarios.

Understanding the dynamic behavior of tires and their interactions with roads plays an important role in designing integrated vehicle control strategies. Accordingly, having access to reliable information about tire–road interactions through tire-embedded sensors is desirable for developing enhanced vehicle control systems. Thus, the main objectives of this research are: 1) to analyze data from an experimental accelerometer-based intelligent tire acquired over a wide range of maneuvers, with different vertical loads, velocities, and high slip angles and 2) to develop a lateral force predictor based on a machine learning tool, more specifically, the Gaussian process regression (GPR) technique. It is determined that the proposed intelligent tire system can provide reliable information about the tire–road interactions even in the case of high slip angles. In addition, lateral force models based on GPR can predict forces very well, outperforming other machine learning models and providing levels of uncertainty that can be useful for designing vehicle control strategies.

This article presents redundancy resolution and disturbance rejection via torque optimization in hybrid cable-driven robots (HCDRs). To begin with, we present a redundant HCDR for nonlinear whole-body system modeling and model reduction. Based on the reduced dynamic model, two new methods are proposed to solve the redundancy resolution problem: 1) joint-space torque optimization for actuated joints (TOAJ) and 2) joint-space torque optimization for actuated and unactuated joints (TOAUJ), and they can be extended to other HCDRs. Compared to the existing approaches, this article provides the first solution (TOAUJ-based method) for HCDRs that can solve the redundancy resolution problem as well as disturbance rejection. Additionally, this article develops detailed algorithms targeting TOAJ and TOAUJ implementation. A simple yet effective controller is designed for analysis and validation. Case studies are conducted to evaluate the performance of TOAJ and TOAUJ. The results show the effectiveness of the aforementioned approaches.

Taking advantage of available measurement in Internet of Things (IoT) for intelligent transportation systems, a sideslip angle estimation method for autonomous vehicles is presented and experimentally verified by fusing global navigation satellite system (GNSS) and inertial measurement unit (IMU), and by constructing an observability index (OI). The correlation between the vehicle sideslip error and the inertial navigation system (INS) heading error is presented first. Then, the observability for the heading error in a velocity-based Kalman filter is discussed and a novel index is defined to check the observability of the heading error. The course from a single antenna GNSS in an autonomous vehicle is augmented to estimate the heading error when the observability of the heading error is low. To reject the course measurement for scenarios that include sideslip movement, a binary hypothesis test approach is applied to indicate whether the vehicle is sidesliping. In addition, based on the OI and the sideslip indicator, a hybrid feedback strategy is designed for the heading error correction. To improve the convergence rate of the heading error in the velocity-based Kalman filter, a tuning strategy is presented. The stochastical observability of the designed Kalman observer is investigated for known and stochastic initial conditions. Finally, the proposed sideslip angle estimator is experimentally validated through a vehicle test platform in critical driving scenarios. The results confirm that the proposed OI can effectively identify when the heading error is observable, and also corroborate the effectiveness of the hybrid feedback strategy and adaptation method in the Kalman observer.

This article presents a novel integrated path-following, crash avoidance, and crash mitigation control algorithm for autonomous vehicles. To improve stability and tracking accuracy of the algorithm in extreme conditions, combined-slip tire forces are considered in the system model. A predictive control framework that monitors slip conditions at each tire is then developed to achieve good dynamics performance by controlling active front steer and brake modulation at each corner. A novel switching mechanism that does not rely on a separate path generation module is designed for avoidance and mitigation phases, which is verified in various harsh driving conditions. Another strong point is the objective function for the crash mitigation phase that is developed based on real-world crash statistics. Simulation results confirm that the proposed algorithm can not only track the desired path in normal driving phase, but also avoid crash and reduce crash severity with ensured vehicle stability.

Controlling the lateral dynamics of an autonomous vehicle confronting a sudden obstacle requires optimal use of tires' force capacities. In these situations, autonomous steering may not be able to respond fast enough to prevent collision or instability. This paper presents an integrated controller for autonomous vehicles, capable of suitably reacting to emergency situations when a sudden obstacle appears on the road. The proposed controller employs differential braking conservatively when needed, to produce an additional yaw moment, thereby improving a vehicle's lateral agility and responsiveness without endangering vehicle stability. A longitudinal controller is also designed to track a desired longitudinal velocity. Model predictive control (MPC) method is used for developing a combined path planning and tracking controller with a hierarchical structure that prioritizes (1) collision avoidance, (2) vehicle stability, and (3) path tracking. The effectiveness of the proposed integrated MPC controller is evaluated by simulating an experimentally validated CarSim model to demonstrate the controller's capability in preventing instability and collisions.

This paper proposes an integrated stability control strategy for tilting vehicles. The work extends the envelope-based lateral stability controller by introducing and enforcing the roll envelope in the optimal control design. The model predictive controller (MPC) scheme is adopted to apply the control effort only when the predicted vehicle states are leaving the safe envelopes. The non-minimum phase problem in active tilting control is handled by utilizing the predictive feature of the controller. It is shown via the simulation in CarSim that, by adopting the envelope-based control scheme, the control effort to maintain the roll stability of narrow vehicles can be greatly reduced. The integrated controller also improves the vehicle handling performance while still guarantees its lateral and roll stability.

This article focuses on the essential limitations of planar point-mass cable-driven parallel robots (CDPRs) in covering all poses of their footprint, which results in concave-shape static workspaces (SW) and also providing a zero force level on the borders of such a SW. Accordingly, a novel passive counterbalancing mechanism is proposed which not only expands SW to fully cover the footprint but also enables CDPR's platform to balance a desired minimum force magnitude in any arbitrary direction all over the SW. Maximizing such force magnitude is defined as an optimization problem which is used to find the optimal values of the proposed mechanism's design parameters. By comparing the SW of different CDPRs with and without the proposed mechanism, effectiveness of the proposed approach is demonstrated. In some examples, it is shown that the effects of the proposed method on the SW size increment is more than doubling the size and number of actuators. Finally, two experimental setups are presented and tested, where effectiveness of the proposed approach in covering the CDPRs’ footprint and also providing the desired minimum force level over the SW are demonstrated.

Considering a simplified model of cables is an essential assumption in the design of state-estimator and vibration control of cable-driven parallel robots (CDPRs). Such an assumption however, impacts the effectiveness of controllers and state-estimators in such systems. This article presents a reaction-based stabilizer for nonmodel-based vibration control of CDPRs to address such model dependence. It is proved analytically that by using only three actuators and not involving the cable-connected winches, the proposed stabilizer regulates all undesired vibrations of the platform. It is also shown that the proposed stabilizer needs only the directly measurable position and velocity of its actuators to form its closed-loop control feedback signals. Effectiveness of the proposed system is more significant in CDPRs with considerable nonlinear effects of cables, where such effects are computationally costly to model, such that the performance of real-time controllers and state-estimators are spoiled. To provide a case study, a multibody nonlinear dynamic model of a suspended CDPR equipped-with the proposed stabilizer with zero cable damping effects is developed in SimMechanics/MATLAB, where a fine-tuned proportional-derivative controller is applied on each actuator to suppress all vibrations of the system. In addition, a suspended CDPR with extremely stretchable nonlinear-stiffness elastomer cables is fabricated and tested, where performance of the proposed stabilizer to suppress the high-amplitude nonlinear oscillations of the whole system is demonstrated.

Tire-road friction coefficient is an essential parameter in vehicle safety control systems. In particular, friction information is required by antilock braking systems (ABS) during deceleration and by traction control systems (TCS) during acceleration. The characteristic of the force acting on the tires has an extremum, which is dependent in the road condition. This paper develops a recursive least squares (RLS) based extremum seeking algorithm that estimates the optimum slip ratio on-line to produce maximum deceleration/acceleration. Results of simulation studies in both Matlab and CarSim environments are presented to illustrate the effectiveness of the developed algorithm and numerically compare with gradient based estimation.

This article focuses on the static workspace (SW) of aerial cable towed robots (ACTRs) with land-fixed winches and provides an optimization approach to maximize the size of such a workspace. In the structure of the studied robots, land-fixed winches beside the ACTRs, actuated by unmanned aerial vehicles (UAVs), are used to manipulate a platform to reach high-altitude poses and balance platform's interaction force/moment in such poses. Capability of UAVs in choosing and holding different positions and orientations enables the studied robots to adapt their available net wrench set (AW) to various required net wrench sets. In order to find the SW of ACTRs with land-fixed winches, at first, AW of a generic robot for a given arrangement of UAVs is developed analytically. Then, a geometrical approach is provided to find all collision-free arrangements of the UAVs. Based on that, a performance index is derived and optimized to find an optimal collision-free arrangement of the UAVs, which maximizes the magnitude of the force that can be balanced by the platform in any arbitrary direction. Finally, the application of the proposed optimization approach in size maximization of the SW is shown in an example.

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.

A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.

This paper provides a solution for the problem of uncontrollable modes in planar cable-driven parallel robots. The proposed solution requires the addition of two unbalanced-rotational-inertia actuators to the end-effector. It is demonstrated both analytically and experimentally that by adding two reaction-based unbalanced-rotational-inertia actuators, the end-effector vibrations can be effectively regulated in the three nonplanar directions that are uncontrollable by the in-plane cables. It is shown that the proposed method is simple, effective, and readily applicable to any planar cable-driven parallel robot.

A path planning approach to generate a path which mitigates the effects of an inevitable crash for autonomous vehicles is presented in this brief. The model predictive control algorithm is adopted here for path planning. The artificial potential field, which describes the obstacles and the potential crash severity, are added to the control objectives to avoid the obstacle, and also to mitigate the inevitable crash. The vehicle dynamic is also considered as an optimal control objective. Based on the analysis above, the model predictive controller can guarantee the command following, obstacle avoidance, vehicle dynamics, and mitigate the inevitable crash. Simulation results verified that the proposed MPC has the abilities of obstacles avoidance and mitigation of the inevitable crash.

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.

Artificial potential fields and optimal controllers are two common methods for path planning of autonomous vehicles. An artificial potential field method is capable of assigning different potential functions to different types of obstacles and road structures and plans the path based on these potential functions. It does not, however, include the vehicle dynamics in the path-planning process. On the other hand, an optimal path-planning controller integrated with vehicle dynamics plans an optimal feasible path that guarantees vehicle stability in following the path. In this method, the obstacles and road boundaries are usually included in the optimal control problem as constraints and not with any arbitrary function. A model predictive path-planning controller is introduced in this paper such that its objective includes potential functions along with the vehicle dynamics terms. Therefore, the path-planning system is capable of treating different obstacles and road structures distinctly while planning the optimal path utilizing vehicle dynamics. The path-planning controller is modeled and simulated on a CarSim vehicle model for some complicated test scenarios. The results show that, with this path-planning controller, the vehicle avoids the obstacles and observes road regulations with appropriate vehicle dynamics. Moreover, since the obstacles and road regulations can be defined with different functions, the path-planning system plans paths corresponding to their importance and priorities.

In this paper, a novel soft inflatable arm is proposed for telepresence robots. The new proposed structure of the arm is achieved by a very common and low-cost inflatable material and it is very light, weighing only about 50 g. However, it can realize agile movement by driving six tiny cables installed in the shoulder and elbow joints. The soft inflatable arm can work by pumping air at a very low pressure (7.32 ± 3.45 kPa) and allows direct and soft human contact without any external force sensors. This paper proposes joint compressed models, joint kinematic models, and kinematic models for the whole inflatable robot arm. These models can be easily applied to multijoint arms. In addition, a new redundancy resolution method is also developed for the inflatable arm, which makes it easier to control resolution and is less complex than other traditional approaches. Numerous experiments have been conducted, including performances of accuracy, repeatability, motion trajectories, human-safe interaction, and remote interaction. Results are satisfactory and validate the expected performance of the proposed robotic arm.

Cable-actuated parallel manipulators (CPMs) rely on cables instead of rigid links to manipulate the moving platform in the taskspace. Upper and lower bounds imposed on the cable tensions limit the force capability in CPMs and render certain forces infeasible at the end effector. This paper presents a geometrical analysis of the problems to 1) determine whether a CPM is capable of balancing a given wrench within the cable tension limits (feasibility check); 2) minimize the 2-norm of the cable tensions that balance feasible wrenches; and 3) check for the existence of an all-positive nullspace vector, which is a necessary condition to have a wrench-closure configuration in CPMs. The unified approach used in this analysis is systematic and geometrically intuitive that is based on the formulation of the static force equilibrium problem as an intersection between two convex sets and the application of Dykstra's alternating projection algorithm to find the projection of a point onto that intersection. In the case of infeasible wrenches, the algorithm can determine whether the infeasibility is because of the cable tension limits or the non-wrench-closure configuration. For the former case, a method was developed by which this algorithm can be used to extend the cable tension limits to balance infeasible wrenches. In addition, the performance of the algorithm is explained in the case of incompletely restrained cable-driven manipulators and the case of manipulators at singular poses. This paper also discusses the algorithm convergence and termination rule. This geometrical and systematic approach is intended for use as a convenient tool for cable tension analysis during design.

In cable-driven parallel manipulators (CPMs), cables can perform only under tension, and therefore, redundant actuation, which can be provided by redundant limbs, is needed to maintain the cable tensions. By optimizing the distribution of the forces in the cables and the redundant limbs, the average size of actuators can be reduced resulting in lower cost. Optimizing the force distribution in CPMs requires consideration for the inequality constraints imposed on the cable forces as a result of the unilateral driving property of the cables. In this study, a projection method is presented to calculate optimum solutions for the actuators force distribution in CPMs. Two solutions are presented: 1) a minimum-norm solution that minimizes the 2-norm of all forces in the cables and redundant limbs and 2) a solution that minimizes the 2-norm of the forces in the cables only. The optimization problem is formulated as a projection on an intersection of convex sets and the Dykstra's projection method is used to obtain the solutions. This method is successfully applied to a 3-DOF CPM.

Cable-based parallel manipulators (CPM) are light-weight manipulators that can reach high accelerations. The difference between the design of CPM and that of rigid-link parallel manipulators is that cables can only perform while under tension. Redundant limbs, such as extra cables, springs, or cylinders, can be used for applying forces on the mobile platform to generate cable tensions resulting in a redundantly actuated manipulator. To operate this manipulator, the actuator-force distribution amongst the cables and the redundant limbs needs to be determined. Actuator-force optimization techniques developed for rigid-link manipulators are unsuitable for CPM. In this study, a numerical procedure based on convex analysis and optimization is presented to calculate the minimum-norm solution that minimizes the 2-norm of actuator forces. The procedure is based on convex optimization that utilizes the Dykstra's alternating projection algorithm to reach to the optimum solution. This numerical method is successfully applied to 3- and 6-degree-of-freedom (DOF) spatial CPMs to determine the optimum actuator forces for a given external load. This study addresses the static analysis in cable-based parallel manipulators in the language of convex analysis

In this paper, the trajectory planning of high-speed cable-based parallel manipulators is studied for a given geometrical path. The time-optimal trajectory-planning technique is adapted for these manipulators in which cable forces must be maintained tensile. This condition is represented as a constraint on the acceleration of the end-effector along the path. The results of this technique are evaluated experimentally on DeltaBot, a cable-based manipulator developed at the University of Waterloo. The performance of the time-optimal technique is examined both for the moving time of the manipulator and the computational time of the trajectory generation.