Model Predictive Control for Full Autonomous Vehicle Overtaking

Crash data in Kentucky show that the fatal crash rate on two-lane rural roads is substantially higher than on any other type of road. Improvements have been proposed at some locations on this type of road which involve either upgrading the existing two-lane road or adding lanes resulting in a four-lane road. As part of the public information process, highway officials have been asked to document the previous results of this type of construction. The objectives of this study were to: 1) identify sections of two-lane rural roadways where either the two-lane road had been realigned and reconstructed or additional lanes had been added and 2) conduct a before-and-after analysis to determine how these changes affected traffic crashes. Of the 49 locations included in the study, 25 involved adding lanes and converting to a four-lane road while the two-lane road was upgraded (realignment with wider lanes and shoulders) at 24 locations. Before the construction, the average traffic volume was almost three times higher on the roads where additional lanes were added than where the two-lane road was upgraded. The average daily traffic increased dramatically after the construction was completed with a slightly higher increase for roads where lanes were added. When all the locations are considered, there was a 51% reduction in the crash rate when the road was upgraded and a 56% reduction in the crash rate when lanes were added. The rate was reduced from 250 to 122 crashes/100 million vehicle miles (MVM) when the road was upgraded and from 258 to 114 crashes/100 MVM when lanes were added. When only the number of crashes is considered, the number of crashes per mile decreased by 39% when the road was upgraded and by 45% when lanes were added. The rate of injury or fatal crashes was reduced by 54% for upgrading the road and 55% by adding lanes, while the number of crashes per mile decreased by 43% both when the road was upgraded and when lanes were added. The overall conclusion of the study is that both upgrading two-lane rural roads and converting the road to four lanes are effective methods of reducing total crashes and injury or fatal crashes. The traffic volume would determine the appropriate alternative.

Greater prosociality toward other human drivers than autonomous vehicles: Human drivers’ discriminatory behavior in mixed traffic

Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach

Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with other vehicles to identify or create a gap and safely merge into. In this paper, we consider the problem of autonomous vehicle control for forced merge scenarios. We propose a novel game-theoretic controller, called the Leader-Follower Game Controller (LFGC), in which the interactions between the autonomous ego vehicle and other vehicles with a priori uncertain driving intentions is modeled as a partially observable leader-follower game. The LFGC estimates the other vehicles’ intentions online based on observed trajectories, and then predicts their future trajectories and plans the ego vehicle’s own trajectory using Model Predictive Control (MPC) to simultaneously achieve probabilistically guaranteed safety and merging objectives. To verify the performance of LFGC, we test it in simulations and with the NGSIM data, where the LFGC demonstrates a high success rate of 97.5% in merging.

Autonomous vehicles (AVs) will coexist with human-driven vehicles (HVs) for a long transition period. During this time, traffic safety may be compromised as various styles of AVs increase the uncertainty of surrounding human drivers’ risk perception and decision-making. This study conducted a cooperative driving simulator experiment to investigate the impact of different AV styles on drivers’ risk perception in overtaking situations. Thirty-two drivers participated, experiencing varying levels of AV aggressiveness and velocity. Verbal protocol analysis assessed drivers’ risk perceptions and risk avoidance decisions. SHapley Additive exPlanations were utilized to identify the factors influencing subjective and objective risk levels. The results indicated that subjective risk levels are primarily influenced by relative movements, while objective risk levels are predominantly affected by the movements of AVs. This study lays the groundwork for improving AV trajectory planning and assisting human drivers in adapting to interactions with AVs.

This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters.

Autonomous vehicle control based on receding horizon method

Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part I: Modeling and solution algorithm design

Driving on public roads is inherently an interactive task, i.e., autonomous vehicles’ (AVs) actions will influence nearby traffic participants’ reactions, and vice versa. Decision-making for AVs in highly interactive driving scenarios (e.g., dense traffic) requires accurately forecasting the impact of the AVs’ intention on nearby traffic participants’ motion. To this end, a hierarchical decision-making framework (HDM) is proposed to navigate through interactive scenarios safely and efficiently. Specifically, the upper layer of the HDM serves as a coarse-level policy generator, which utilizes the plan-informed graph attention network (P-GAT) to provide interaction-aware guidance. The P-GAT predictor takes the historical states of nearby traffic participants, road structure information, and AVs’ potential intentions as inputs. Subsequently, it predicts the motions of other traffic participants in response to potential actions of the AVs, which is then systematically evaluated to generate interactive guidance. Furthermore, the learned policy is utilized to guide the lower layer, which utilizes a fine-level model predictive control (MPC)-based planner to ensure safety and kinematic feasibility. Finally, to validate the effectiveness of HDM, both qualitative and quantitative experiments are carried out. More importantly, the hardware-in-the-loop (HiL) experiment is also implemented, including mandatory lane change in dense traffic flow and interaction with the human driver. The results demonstrate that the proposed HDM can improve driving safety and efficiency compared with baselines.

The use of traffic simulation to analyze traffic safety and performance has become common in transportation engineering. Microsimulation methods are increasingly used to analyze driving performance for different road geometries and environmental elements. Drivers’ perception has an important impact on driving performance factors contributing to traffic safety on transportation facilities (highways, arterials, intersections, etc.). Impaired vision leads to failure in drivers’ perception and making right decisions. Various studies investigated the impact of environmental elements (fog, rain, snow, etc.) on driving performance. However, there is limited research examining the potentially detrimental effects on driving capabilities due to differing exposure to natural light brightness, in particular sun exposure. Autonomous vehicles (AVs) showed a significant impact enhancing traffic capacity and improving safety margins in car-following models. AVs may also enhance and/or complement human driving under deteriorated driving conditions such as sun glare. This study uses a calibrated traffic simulation and surrogate safety assessment model to improve traffic operations and safety performance under impaired visibility using different types of autonomous vehicles. A combination of visibility reduction, traffic flow characteristics, and autonomy levels of AVs was simulated and assessed in terms of the number of conflicts, severity level, and traffic operations. The simulation analysis results used to reveal the contribution of conflicts to the risk of crashes varied based on the influence of autonomy level on safe driving during sun glare exposure. The outcome of this study indicates the benefits of using different levels of AVs as a solution to driving under vision impairment situations that researchers, traffic engineers, and policy makers can use to enhance traffic operation and road safety in urban areas.

In the medium-term, the number of semi-autonomous vehicles is expected to rise significantly. These changes in vehicle capabilities make it necessary to analyze their interaction with road infrastructure, which has been developed for human-driven vehicles. Current systems use artificial vision, recording the oncoming road and using the center and edgeline road markings to automatically facilitate keeping the vehicle within the lane. In addition to alignment and road markings, lane width has emerged as one of the geometric parameters that might cause disengagement and therefore must be assessed. The objective of this research was to study the impact of lane width on semi-autonomous vehicle performance. The automatic lateral control of this type of vehicle was tested along 81 lanes of an urban arterial comprising diverse widths. Results showed that the semi-autonomous system tended to fail on narrow lanes. There was a maximum width below which human control was always required—referred to as the human lane width—measuring 2.5 m. A minimum width above which automatic control was always possible—the automatic lane width—was established to be 2.75 m. Finally, a lane width of 2.72 m was found to have the same probability of automatic and human lateral control, namely the critical lane width. Following a similar methodology, these parameters could be determined for other vehicles, enhancing the interaction between autonomous vehicles and road infrastructure and thus supporting rapid deployment of autonomous technology without compromising safety.

Due to the uncertainty of vehicle model parameters, modeling errors and external disturbances, the performance of path tracking control system is poor, especially under high velocity and large curvature extreme conditions. To address this issue, this paper presents a novel gain-scheduled robust control strategy based on linear parameter varying system with model predictive control (MPC) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> . Firstly, fully considering the influence of the time-varying characteristics of vehicle velocity and tire cornering stiffness on the path tracking system model, a novel linear parameter varying system model is built for path tracking control of autonomous vehicle. Then, a path tracking robust controller is designed based on gain-scheduled approach, and the linear matrix inequality (LMI) is applied to solve optimization problem, in which MPC and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> robust control theory are applied to the controller design process. Finally, the simulation experiments have verified that the proposed novel robust control strategy can improve the path tracking accuracy and ensure the vehicle lateral and roll stability, especially under high velocity and large curvature extreme conditions. Meanwhile, the proposed robust controller shows superiority to suppress the parameters uncertainty, modeling error and external disturbance.

The model predictive control method can be effective for converter control in distributed power generation but requires a large amount of computation, leading to a considerable time delay in the actuation. If the delay is not reflected, the system performance could get worse. This paper presents a two-step (horizon) prediction algorithm of model predictive control technique for grid-tied inverters used in wind turbine systems. The control objectives such as active and reactive power flow and switching loss reduction are reflected in the objective function of the controller. The proposed model predictive control strategy is verified numerically by using MATLAB/Simulink.

Manipulating constraints on driver-cyclist interactions in a fixed travel space: Effects of road configuration on drivers’ overtaking behavior

Rapid advancements in vehicle automation technology have enabled autonomous vehicles (AVs) to move beyond simple, structured highway settings and operate increasingly within more complex urban environments. As AVs become more prevalent, they will inevitably share the road with diverse traffic participants, particularly pedestrians. The high uncertainty and dynamic nature inherent in human behavior significantly complicate the AV’s decision-making process. To ensure both safety and efficiency while exhibiting socially acceptable behavior, AVs must make real-time decisions and continuously adapt their strategies in response to surrounding pedestrian behaviors. This poses a major challenge to existing AV decision-making systems. Therefore, this study focuses on developing the decision-making strategies for AVs in urban pedestrian-involved environments, spanning scenarios from simple interactions with a single pedestrian to complex cases involving multiple pedestrians. The core objective is to uncover the underlying interaction dynamics and to improve the AV’s capability to make decisions that are safe, efficient and socially acceptable in dynamic, human-centric contexts. Initially, this study investigates the critical factors influencing the decision making processes of human drivers and pedestrians during vehicle–pedestrian interactions. A series of controlled experiments were conducted using a virtual reality platform designed to collect realistic behavioral data. The data analysis mainly focused on kinematic variables that are easily measurable in real-world deployments. AdaBoost classification and Partial Dependence Plots were employed to identify and visualize the most influential factors affecting pedestrian crossing intentions and driver approaching behaviors. The results indicate that longitudinal distance and vehicle acceleration are the most influential factors in pedestrian decision-making, while pedestrian speed and longitudinal distance also play a crucial role in determining whether the vehicle yields or not. Based on these insights, a simplified mathematical model was developed to relate observable kinematic parameters to pedestrian crossing intentions, providing a practical tool for dynamically inferring crossing intentions during interactions. Additionally, the study explored driver yielding patterns under varying degrees of pedestrian intention clarity. These findings support the development of interpretable and implementable models for real-time decision-making in vehicle–pedestrian interaction scenarios. In addition, this study examines the decision-making strategies of the AVs when interacting with a single pedestrian in unsignalized intersections. A novel framework was proposed that integrates the Partially Observable Markov Decision Process with behavioral game theory to dynamically model interactive behaviors between the AVs and the pedestrian. Both agents were modeled as dynamic-belief-induced quantal cognitive hierarchy models, considering human reasoning limitations and bounded rationality in the decision-making process. Moreover, a dynamic belief updating mechanism allowed the AV to update its understanding of the opponent’s rationality degree in real-time based on observed behaviors and adapt its strategies accordingly. The analysis results indicate that proposed models effectively simulate vehicle-pedestrian interactions and the AV decision-making approach performs well in safety, efficiency, and smoothness. It captures key patterns of the driving behavior operated by real human drivers in virtual reality experiments and even achieves more comfortable navigation compared to our previous virtual reality experimental data. Finally, this study investigates the decision-making strategies of the AVs operating in pedestrian-rich shared spaces. A novel framework was proposed for modeling interactions between the AV and multiple pedestrians. In this framework, a cognitive process modeling approach inspired by the Free Energy Principle was integrated into both the AV and pedestrian models to simulate more realistic interaction dynamics. Specifically, the proposed pedestrian Cognitive-Risk Social Force Model adjusts goal-directed and repulsive forces using a fused measure of cognitive uncertainty and physical risk to produce human-like trajectories. Meanwhile, the AV leverages this fused risk to construct a dynamic, riskaware adjacency matrix for a Graph Convolutional Network within a Soft Actor-Critic architecture, allowing it to make more reasonable and informed decisions. The qualitative and quantitative results indicate that the proposed strategy effectively improves safety, efficiency, and smoothness of AV navigation compared to the state-of-the-art method. In conclusion, this study addresses key challenges in AV decision-making within human-centric urban environments, providing valuable insights as well as practical solutions to support safe, efficient, and socially aware autonomous navigation.