Autonomous vehicles platooning on highways can save energy costs, improving traffic safety and efficiency. Vehicles take corresponding actions in response to various transportation environments with an embedded dynamics model. However, it is unrealistic to expect that we have all accurate dynamics models due to the heterogeneity of vehicles. This work proposes a physics and data co-driven framework for heterogeneous vehicle platoon control by integrating physics-informed machine learning (PIML) with model predictive control (MPC). The proposed method targets scenarios where only limited observations (data) of vehicle dynamics and partially known physics are available for use. The partially known physics serves as the prior knowledge to penalize the training of the neural network resulting in the physics informed neural networks (PINN), with which we can not only reduce the variance of the neural network but also restore the missing physics during the training of the neural network. We demonstrate the proposed method with two scenarios in the case study. The results show that: 1) the neural network penalized by the partially known physics can accurately forecast the vehicle states in certain time ranges, 2) the missing vehicle dynamics parameters are recovered, and 3) the proposed PINN-MPC outperforms conventional data-driven MPC (NN-MPC) without physics constraints. The demonstration manifests that PINN-MPC has great potential to be used for heterogeneous platoon control with reduced data acquisition cost and only partially known physics.

The vehicle platoon control for highway traffic can help to improve traffic flow efficiency, enhance traffic safety, and reduce fuel consumption. In previous platoon control research, most of the driving behaviors are described using deterministic car-following models. Nevertheless, random factors that may come from the vehicle power train and additional stimuli, can have a greater impact on platoon control in highway traffic. In this paper, a novel stochastic control method is proposed based on a stochastic car-following model which considers microscopic driving behavior. Firstly, the stochastic car-following model is designed that fully accounts for the impact of random factors on platoon control. Secondly, an optimal objective function is constructed and the Hamilton-Jacobi-Bellman equation is used to solve this stochastic control problem, thereby completing the upper-level controller design and obtaining the optimal desired acceleration of the vehicle. Thirdly, the stochastic stability method is applied to analyze the proposed model and obtain the stochastic stability conditions satisfied by the model. Finally, tests are conducted for three different scenarios: stable speed, acceleration, and deceleration with additive and multiplicative noise, as well as the case where the lead vehicle’s speed is based on real vehicle trajectory data. These tests validate the stability and effectiveness of the stochastic car-following model predictive control method from the perspective of control strategy and model respectively. The experimental results show that under the stable parameter conditions of the model, the connected and automated vehicle platoon can achieve accurate speed tracking and maintain an appropriate safe distance in highway system.

The application of cooperative adaptive cruise control (CACC) in vehicle platoons with vehicle-to-vehicle (V2V) communication has received significant interest. CACC systems ensure string stability, enabling vehicles to maintain close inter-vehicle distances, thereby mitigating traffic jams and reducing fuel consumption. However, in realistic scenarios, vehicle platoons are typically heterogeneous, featuring diverse powertrain dynamics and uncertain parameters. To extend linear CACC systems that assume prior knowledge of vehicle parameters, novel adaptive controllers are developed. These controllers accommodate nonlinear and heterogeneous vehicle platoons with parametric uncertainties. To guarantee uniformly global asymptotic stability in nonlinear CACC systems, two innovative parameter updating laws are proposed and validated using Lyapunov’s method. Additionally, a data selection and updating algorithm is implemented to construct a positive definite matrix and enhance the convergence rate of CACC controllers. Subsequently, examining transient behavior provides insights into tuning the adaptive controller gains. Finally, simulation and experimental results illustrate that both the baseline adaptive and novel adaptive controller exhibit superior state tracking compared to the feedback linearizing PD controller with nominal parameters. In addition, the novel adaptive controller presents enhanced estimation accuracy for unknown parameters, ensuring improved robustness in practical vehicle platoon applications. String stability evaluation in the frequency domain for a nonlinear CACC system is realized through the innovative adaptive control scheme.

This study constructs a three-degree-of-freedom (3-DOF) dynamics model for a Self-Propelled Modular Transporter (SPMT) with six axles. A novel particle swarm optimization with variable control factors and elitist learning (PSO-VFEL) is developed to quantify unknown terms and is compared with three existing optimizers, including an initial PSO, a traditional PSO, and a PSO with variable control factors (PSO-VF). Compared to the existing optimizers, the PSO-VFEL demonstrates higher flying flexibility and particle diversity, attributed to the generationally variable factors and additional guidance from the elitist learning strategy. Simulation data from the Carla software is employed for model calibration. Results show that the developed PSO-VFEL is superior for calibrating the dynamics model, achieving fitness improvements of 31.852%, 20.231%, and 5.154% compared to the initial PSO, the traditional PSO, and the PSO-VF, respectively. This research provides a dynamics model and a complementary calibrator for engineering applications.

To optimize a vehicle platoon system in terms of car-following behavior, a decentralized model predictive control (MPC) strategy for longitudinal velocity control was established (namely, CF-MPC). Firstly, considering the influence of car-following behavior on vehicle states, a longitudinal velocity control model for platoons of connected and automated vehicles (CAV) was designed. Based on that model, an upper-level MPC controller was built to obtain the desired acceleration of the vehicles. Secondly, a lower-level controller received the desired acceleration signal and converted it into the expected throttle opening/braking pressure, to control acceleration/deceleration. Then, the Lyapunov stability method was used to detect the stability conditions that the model should satisfy. Finally, three simulation procedures—constant speed, acceleration, and deceleration were tested, and the validity of the CF-MPC method was verified from the perspectives of a model strategy and a control strategy. The simulation results show that with the proposed CF-MPC method, CAV platoons quickly completed velocity tracking and maintained a safe distance, thereby improving traffic efficiency, fuel economy, driving safety, and transportation capacity.

The fuel efficiency of the transportation sector has become a key factor to reduce greenhouse gas emissions and fuel consumption in response to the negative impacts of global warming. As an approach to energy saving and environmental sustainability, eco-driving has attracted considerable research interest in the past decades. This review aims to provide a comprehensive review of the research on eco-driving using methodologies of literature bibliometrics and content analysis through VOSviewer software. The following keywords “ecological-driving”, “ecological-routing”, “ecological-bus”, “ecological-car”, “ecological-vehicle”, “eco-driving”, “eco-routing”, “eco-driver”, “eco-bus”, “eco-car” and “eco-vehicle” are used for paper retrieval. The query was conducted on January 20, 2021. The results take account of all journal articles, proceedings papers, and reviews without time limitation. Finally, a total of 767 documents were retrieved as total publications, which were viewed over the period 2001–2020 based on the Web of Science (WoS) Core Collection database. The publication year, leading countries, leading sources, leading institutions, leading authors, document citation, and document co-citation were analyzed to explore the primary trends. The In-depth analysis reveals five clusters of keywords, and the review of relevant studies on eco-driving from five different perspectives is carried out to identify potential trends and future research hot spots of eco-driving.

Side collisions caused by sudden vehicle cut-ins comprise a significant proportion of traffic accidents. Due to the complex and dynamic nature of traffic environments, the warning algorithms in advanced driving assistant systems (ADAS) often misjudge and misdiagnose risk and omit necessary warnings, because they rely solely on the sensing information of the single vehicle equipped with ADAS and have limited insights from and communication with the surrounding vehicles and traffic environment. To improve the effectiveness of ADAS in cut-in scenarios, this study established a collision warning model in a vehicle-to-vehicle (V2V) communication environment. Firstly, based on the support vector machine-recursive feature elimination (SVM-RFE) lane-change intent-recognition model, the lane-change feasibility and the change rate of the lateral offset, the logical “and” was used to establish a lane-change behavior prediction model, and a trajectory prediction model was established based on the long short-term memory (LSTM). Then, based on the proposed comprehensive prediction model for lane-change behavior, the driving trajectory prediction model, and the oriented bounding box (OBB) detection algorithm, a collision warning model was established for a V2V environment. Finally, based on a driving simulation platform and a real-world vehicle test, a cut-in experiment in a V2V environment was designed and implemented. By comparing the warning confusion matrix and warning time, it was found that the proposed cut-in collision warning model is superior to the traditional collision warning model. The results of this study can provide new modeling ideas and a theoretical basis for ADAS to further optimize for a cut-in scenario.

This article presents a framework to determine driving style and design a driver steering model considering driver characteristics. First, principal component analysis (PCA) and$K$-means clustering are utilized to classify 30 participants into cautious, moderate, and aggressive drivers. Subsequently, a generic steering model is established based on the model predictive control method. Thereafter, the maximum lateral acceleration is extracted as a crucial indicator to represent driver characteristics, and it is calibrated through probabilistic models using the dataset, which consists of the classified drivers. Besides, point estimation model and interval estimation model are leveraged to determine driving style and adjust constraints in the stochastic programming-based steering model. Finally, simulation experiments present the variations of actual output trajectories between the aggressive drivers and the cautious drivers.

This study combines applied mathematics, visual analysis technology, information science with an approach of Scientometrics to systematically analyze the development status, research distribution and future trend of intelligent vehicles research. A total number of 3933 published paper index by SCIE and SSCI from 2000 to 2019 are researched based on Mapping Knowledge Domain (MKD) and Scientometrics approaches. Firstly, this paper analyzes the literature content in the field of intelligent vehicles by including the literature number, literature productive countries, research organization, co-authorship of main research groups and the journals from which the articles are mainly sourced. Then, co-citation analysis is used to obtain five major research directions in the field of intelligent vehicles, which include “system framework”, “internet of vehicles”, “intersection control algorithms”, “influence on traffic flow”, and “policies and barriers”, respectively. The keyword co-occurrence analysis is applied to identify four dominant clusters: “planning and control system”, “autonomous vehicle questionnaire”, “sensor and vision”, and “connected vehicles”. Finally, we divide burst keywords into three phases according to the publication date to show more clearly the change of research focus and direction over time.

Cooperative driving systems may increase the utilization of road infrastructure resources through coordinated control and platooning of individual vehicles with the potential of enhancing both traffic safety and efficiency. Vehicle cooperative driving is essentially a hybrid system that is a combination of discrete events, i.e., the transition of discrete cooperative maneuvering modes, such as vehicle merging and platoon splitting, as well as continuous vehicle dynamics. In this paper, a novel hybrid system consisting of the discrete cooperative maneuver switch and the continuous vehicle motion control is introduced into a multi-vehicle cooperative control system with a distributed control structure, leading each automated vehicle to conduct path planning and motion control separately. The primary novelty of this paper lies in that it presents a control algorithm combining artificial potential field (APF) approach with model predictive control (MPC), and using the optimizer of the MPC controller to replace the gradient-descending method in the traditional APF approach. Such a method can accomplish both path planning and motion control synchronously. Second, based on hybrid automata, a cooperative maneuver switching model consisting of a system state set and a discrete maneuver transition rule is established for two discrete maneuvers in the cooperative driving system, i.e., single-vehicle cruising and multiple-vehicle platooning. Simulations in several typical traffic scenarios demonstrate the effectiveness of the proposed method.

This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field.

Vehicle behavior learning can be used in video surveillance systems to identify normal and abnormal vehicle motion patterns for the management of traffic operations, public services, and law enforcement. The purpose of this paper is to develop a novel adaptive sparse reconstruction method for vehicle behavior learning based on video surveillance systems. First, the ℓ0 minimization problem of sparse reconstruction is relaxed to the ℓp minimization problem (0 <; p <; 1). A hybrid algorithm orthogonal matching pursuit-quasi-Newton is proposed to effectively find the sparse solutions. Then, a sparse reconstruction and similarity-based trajectory classifier is developed to learn vehicle behavior based on the sparse solutions and the trajectory similarity. In order to validate the performance and the effectiveness of the proposed method, four datasets, including CROSS, i-LIDA, Stop Sign, and I5 are used in the experiments. The results show that the classification and the anomaly detection accuracies of the proposed method are superior to the representative methods, including the Naïve Bayes classifier, k nearest neighbor, support vector machine, and traditional sparse reconstruction-based trajectory learning methods.

Accidents such as those caused by rollovers and sideslips in complex driving environments involving heavy-duty vehicles (HDVs) often have serious consequences. Such accidents can be due to many factors. In this paper, a probabilistic method for predicting and preventing these accidents is presented. First, a specific vehicle dynamics model based on various random parameters that consider the wind velocity and road curvature is developed. Second, a safety margin function is defined to divide the safe and dangerous domains in the parameter space. Then, the first-order reliability method and second-order reliability method approximations are developed to evaluate the probability of such an accident by using the vehicle dynamics model. Finally, the probability model is applied to explore the interrelations and sensitivities of those parameters with regard to their effects on the accident probability in different scenarios. The study suggests that the presented probabilistic methodology can effectively estimate rollovers and sideslips of HDVs in complex environments, which represent a challenge for the prediction of accidents based on sensors alone.

In this study, we developed a microcosmic mobile emissions model based on an intelligent agent model of vehicles. The intelligent agent was first introduced into a micro-traffic flow system. Individual differences in driver behavior were considered, and the theory of probability was applied to reflect the distribution of drivers' stochastic characteristic dispositions. Each vehicle expressed its intelligence through its own character by perceiving the leading vehicle. From an operational perspective, differences in drivers' dispositions were reflected by a weighted coefficient. Finally, a hybrid microcosmic mobile emissions model was proposed. Its coefficients were determined using traffic data and experiments. Because it addresses more aspects of the car-following process, this model is theoretically superior to previous models, as verified by a numerical simulation. The proposed model was applied to a case study of the emissions from ten vehicles in an urban setting. The model effectively estimated mobile emissions rates. The results indicate that the model can reflect individual differences among drivers and demonstrate that reckless drivers generate more emissions.