Longitudinal Vehicle Control Research Articles

First Observations about Response Times and Connectivity in a Vehicles Platooning Experiment

A key question about cooperative vehicle longitudinal control is reactivity, which determines the future of road safety, and capacity. In adaptive cruise control (ACC), the controller adapts the speed of the vehicle to its immediate leader’s speed whereas, in the cooperative version (CACC), connectivity between the platoon equipped vehicles reduces their response times. The USDoT Cooperative Automated Research Mobility Applications (CARMA) platform provides data for platooning experiments involving ACC and CACC vehicles. We measure ACC response times (mean = 2.78 seconds) larger than for human-driven cars. We study response times inside CACC platoons showing that connectivity is not always effective.

Composite nonlinear extended state observer and its application to unmanned ground vehicles

In this paper, a disturbance-observer-based control strategy is proposed for longitudinal control and lateral control of unmanned ground vehicles. A composite nonlinear extended state observer is put forward to estimate the total disturbance. A longitudinal dead-zone model is developed with actual constraints and rapidity requirements for unmanned ground vehicles. The disturbance-observer-based feedback controller is presented to improve the robustness of the closed-loop system by incorporating the composite nonlinear extended state observer. Numerical and experimental results are given to verify the effectiveness of the proposed control strategy.

Longitudinal Vehicle Control based on Off-road Tire Model for Soft Soil Applications

Abstract Autonomous off-road vehicles such as tractors, military vehicles, construction and agricultural machinery require an important power traction to face off mobility issues on soft soils and rough terrains. This paper presents a powertrain controller developed using an adapted Burckhardt tire model based on a Bekker-Wong wheel-soil interaction model according to the plasticity theory and soil mechanics. A Gain Scheduling approach allows the vehicle to maintain the desired reference speed while avoiding the impact of ground conditions. Simulation results show vehicle mobility control through different types of soils.

Energetic Impacts Evaluation of Eco-Driving on Mixed Traffic With Driver Behavioral Diversity

This study presents a fuel-economical driving strategy for connected and automated vehicles (CAVs) and investigates its impacts on human-driven platoon’s fuel efficiency, considering driver behavioral diversity. In mixed traffic where a limited number of CAVs and many human-driven vehicles share the road, the Eco-Driving strategy of CAVs, accomplished through vehicle connectivity and longitudinal dynamics control, can significantly reduce fuel consumption of CAVs, particularly during transient traffic conditions, by avoiding unnecessary braking and acceleration maneuvers which lead to excessive fuel consumption. This is implemented and validated by a vehicle longitudinal dynamic control design with an instantaneous power-based fuel rate estimation model. Moreover, such an Eco-Driving strategy will also help most of the following human-driven vehicles to improve fuel performance despite a highly diverse and uncertain driving characteristics spectrum. The car-following behaviors of a human-driven platoon are described by a microscopic traffic model with dedicated representations of the individual drivers’ preferences. A comprehensive statistical investigation shows the fuel-saving benefits for a human-driven platoon by adopting the proposed Eco-Driving strategy for CAVs. Some special cases are discussed, as well. The results collectively demonstrate the positive impacts of CAVs, even with a low penetration rate, could potentially have on the energy efficiency of the transportation sector within the foreseeable future.

Optimization of Longitudinal Control of an Autonomous Vehicle using Flower Pollination Algorithm based on Data-driven Approach

Some challenges in the development of autonomous vehicles, such as generating a model representing the dynamics of the speed, designing a longitudinal controller, and the optimization method, are still explored until now. In this paper, a longitudinal controller based on the proportional-integral-derivative controller with an additional feed- forward term is proposed, where the Flower Pollination Algorithm is employed for optimizing the controller. The feed-forward term and the model used in the optimization are generated using the data-driven approach. For the optimization, a cost function considering mean absolute error and mean absolute jerk will be minimized. The simulation study was performed using the CARLA simulator, and the results show that the proposed scheme represents the dynamics of the speed very well inside the range of the training data and does not overfit the training data. It is also demonstrated that the proposed longitudinal controller can track the desired speed satisfactorily in a non-straight path.

Longitudinal Modelling and Control of In-Wheel-Motor Electric Vehicles as Multi-Agent Systems

This paper deals with longitudinal motion control of electric vehicles (EVs) driven by in-wheel-motors (IWMs). It shows that the IWM-EV is fundamentally a multi-agent system with physical interaction. Three ways to model the IWM-EV are proposed, and each is applicable to certain control objectives. Firstly, a nonlinear model with hierarchical structure is established, and it can be used for passivity-based motion control. Secondly, a linearized model with rank-1 interconnection matrix is presented for stability analysis. Thirdly, a time-varying state-space model is proposed for optimal control using linear quadratic regulator (LQR). The proposed modellings contribute the new understanding of IWM-EV dynamics from the view point of multi-agent-system theory. By choosing the suitable control theory for each model, the complexity level of system design is maintained constant, no matter what the number of IWMs installed to the vehicle body. The effectiveness of three models and their design approaches are discussed by several examples with Matlab/Carsim co-simulator.

Alcohol use patterns and their association with sober driver vehicle control in high fidelity driving simulation

Objective To examine the relationship between patterns of alcohol use, as determined by the Alcohol Use Disorders Identification Test, and vehicle control measures in high fidelity driving simulation among adult sober drivers. Methods Baseline data (BAC = 0.00%; N = 108) from a larger study aimed at using high-fidelity driving simulation (National Advanced Driving Simulator) to evaluate the feasibility of vehicle-based sensors to identify alcohol impairment were analyzed. Driving simulation scenarios included driving on urban, interstate, and rural roadways. The independent variable was the pattern of alcohol use measured with the Alcohol Use Disorder Identification Test (AUDIT). Dependent variables included one lateral vehicle control measure (i.e., standard deviation of lane position (SDLP)) and one longitudinal vehicle control measure (i.e., average speed relative to the speed limit) in high fidelity driving simulation. Multivariable linear regression was used to examine the associations between patterns of alcohol use and vehicle control measures. Results Total AUDIT scores ≥8 was positively associated with SDLP. Increased frequency of drinking was associated with decreased SDLP and increased average speed relative to the speed limit. Increased reports of blackouts and alcohol-related injury were associated with increased average speed relative to the speed limit. Driver performance (SDLP, average speed relative to the speed limit) was related to additional factors such as driver experience, age, marital status, and driving context. Conclusions The findings support our hypothesis that the AUDIT score and responses to individual AUDIT questions, among sober drivers, relates to vehicle control measures. Overall, our data highlight two important themes: 1) a need to further integrate alcohol use metrics with high-fidelity driving simulation studies to understand how drinking experience can relate to driver behavior and vehicle control and 2) the opportunity to integrate clinical perspectives with driving simulation research to strengthen clinically oriented alcohol-misuse prevention efforts.

End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient Algorithm.

Changing lanes while driving requires coordinating the lateral and longitudinal controls of a vehicle, considering its running state and the surrounding environment. Although the existing rule-based automated lane-changing method is simple, it is unsuitable for unpredictable scenarios encountered in practice. Therefore, using a deep deterministic policy gradient (DDPG) algorithm, we propose an end-to-end method for automated lane changing based on lidar data. The distance state information of the lane boundary and the surrounding vehicles obtained by the agent in a simulation environment is denoted as the state space for an automated lane-change problem based on reinforcement learning. The steering wheel angle and longitudinal acceleration are used as the action space, and both the state and action spaces are continuous. In terms of the reward function, avoiding collision and setting different expected lane-changing distances that represent different driving styles are considered for security, and the angular velocity of the steering wheel and jerk are considered for comfort. The minimum speed limit for lane changing and the control of the agent for a quick lane change are considered for efficiency. For a one-way two-lane road, a visual simulation environment scene is constructed using Pyglet. By comparing the lane-changing process tracks of two driving styles in a simplified traffic flow scene, we study the influence of driving style on the lane-changing process and lane-changing time. Through the training and adjustment of the combined lateral and longitudinal control of autonomous vehicles with different driving styles in complex traffic scenes, the vehicles could complete a series of driving tasks while considering driving-style differences. The experimental results show that autonomous vehicles can reflect the differences in the driving styles at the time of lane change at the same speed. Under the combined lateral and longitudinal control, the autonomous vehicles exhibit good robustness to different speeds and traffic density in different road sections. Thus, autonomous vehicles trained using the proposed method can learn an automated lane-changing policy while considering safety, comfort, and efficiency.

Safety Cruise Control of Connected Vehicles Using Radar and Vehicle-to-Vehicle Communication

It is a common traffic phenomenon that side vehicles change lanes without any forewarning, which is easy to induce vehicle side collision and rear-end collision. In order to improve the active safety of cruise control in such a random traffic environment, this article proposes a connected cruise control (CCC) framework considering merging behavior probability of side vehicles. The control strategy of the framework first uses vehicle-to-vehicle communication and radar recognition algorithm to detect the surrounding vehicle targets. Then, the merging potential of the side vehicle is determined through the combination of fuzzy support vector machine and sliding window detection. Afterward, a cruise distance strategy considering the cutting-in probability of merging behavior is proposed and applied to the CCC system to realize the early braking of the main vehicle. Finally, the longitudinal control of the host vehicle is realized by the acceleration control, considering both the time delay and feedback error. The effectiveness of the proposed systematic control framework is verified through not only simulation but also field test. Results show that our method can accurately detect the lane change intent of the side vehicle, thus realizing braking of the host vehicle in advance when the side vehicle changes lane.

The effect of sleep-need and time-on-task on driver fatigue

Both sleep- and task-related factors are thought to contribute to driver fatigue, with each factor individually associated with deteriorated driving performance. However, the relative and combined effects of these factors in the context of monotonous driving have not been well studied. This study (N = 60) investigated lateral and longitudinal vehicle control, subjective fatigue and physiological response (EEG) during three 10-minute periods of time-on-task spread across a monotonous, 2-hour simulator drive. Level of physiological sleep-need was manipulated between participants by varying the instructed time spent in bed on the night before testing (≤5h or ≥ 8 h). In addition, half of the participants in each sleep group read the applicable speed limit from periodic roadside signs whereas the others performed an arithmetic calculation, displayed on the signs, to determine the speed limit. This task manipulation has been demonstrated to reduce performance decrements over time. Results demonstrated effects of time-on-task and sleep need on self-report ratings, an effect of time-on-task on EEG indices, and an interaction of sleep-need and time-on-task on an EEG index of mental workload and on the lateral control measure of driving performance. There were no significant effects on the measure of speed variability. These results confirm that both sleep-need and time-on-task negatively affect driver state, and that time-on-task decrements in driver performance can occur in the absence of heightened sleep-need. Results also suggest that drivers with heightened sleep-need could protect their performance for a short time, perhaps by exerting effort to compensate for reduced capacity. The secondary task did not counteract declining performance.

Velocity-based Lateral Stability Control for Four-wheel Independently Actuated Electric Vehicles with Homogeneous Polynomial Approach

This paper presents a control strategy to enhance the lateral dynamics stability and handling performance of the four-wheel independently actuated (FWIA) electric vehicles (EVs). The vehicle longitudinal velocity uncertainty and controller saturation are considered, a double layers control scheme is adopted. In the upper layer, the homogeneous polynomial parameter-dependent approach is introduced to track the uncertainty problem, and a multi-objective controller is designed to obtain the desired external yaw moment. In the lower layer, an optimal force distribution method with considering the distribution error and tire workload is employed to allocate the desired external yaw moment into forces of the four in-wheel motors. Simulation results verify the effectiveness of the proposed control strategy.

Study on the driving style adaptive vehicle longitudinal control strategy

This paper presents a fusion control strategy of adaptive cruise control &#x0028 ACC &#x0029 and collision avoidance &#x0028 CA &#x0029 , which takes into account a driver&#x02BC s behavioral style. First, a questionnaire survey was performed to identify driver type, and the corresponding driving behavioral data were collected via driving simulator experiments, which served as the template data for the online identification of driver type. Then, the driver-adaptive ACC &#x002F CA fusion control strategy was designed, and its effect was verified by virtual experiments. The results indicate that the proposed control strategy could achieve the fusion control of ACC and CA successfully and improve driver adaptability and comfort.

Adequacy of Manitoba concrete bridge rail during truck platoon impacts and associated occupant risks

Platooning is an extension of Cooperative Adaptive Cruise Control (CACC) and forward collision avoidance technology; it allows lateral and longitudinal control of vehicles while they move in tight formations. Platooning is ideal for trucks as they usually travel long distances in highways as a group. It is necessary to understand if the existing roadside safety devices are adequate to resist the consecutive impacts at high speed that can occur due to errant truck platoons. It is also important to analyze the associated occupant risks during such impact events. A detailed analysis of the capacity and adequacy of Manitoba Concrete Bridge Rail using computer simulations, under Manual for Assessing Safety Hardware (MASH) Test Level 5 (TL5) criteria, is presented in this study. The study briefly discusses the methodology to run multiple impact computer simulations in LS-DYNA. The impact of the leading truck is validated using the known data from a full-scale crash test; then, the impact events involving the following trucks are simulated to understand the additional capacity of the roadside barrier. To assess the occupant risk, the flail space model (FSM) concept is utilised as per MASH criteria. Simulation results imply that catastrophic barrier failure and major injuries to the occupants are unlikely to occur during the truck platoon impact.

Longitudinal and lateral control of autonomous vehicles in multi‐vehicle driving environments

Lane changes in multi-vehicle driving environments are one of the most challenging manoeuvres for autonomous vehicles. The key innovation of this study is to develop an integrated longitudinal and lateral trajectory planning and tracking control algorithm under vehicle-to-vehicle communication. This algorithm includes two levels: trajectory planning and path-following control. In the upper level, considering riding comfort, a collision-free lane-changing trajectory cluster is generated under different lane change durations. Then, the most appropriate trajectory from this cluster is provided by selecting the optimal lane change duration considering vehicle dynamics safety, collision avoidance of surrounding vehicles and driver preference. At the bottom level, a multiple-input multiple-output triple-step non-linear approach is proposed in the longitudinal and lateral path-following controller design. The stability of the closed-loop system is rigorously proven based on the Lyapunov function. Finally, the effectiveness of the proposed algorithm is verified with a high-fidelity and full-car model on the veDYNA platform.

Simplified longitudinal control of air-breathing hypersonic vehicles with hybrid actuators

This paper considers the longitudinal compound control of air-breathing hypersonic vehicles configured with hybrid aerodynamic control surface (ACS) and reaction control system (RCS). In the altitude loop, the proposed nonadaptive back-stepping design results in a simplified ACS-RCS compound control. This is attributable to the introduced tangent tracking errors and ACS-RCS fusion algorithm. ACS nonlinearity, RCS quantization, and modeling parametric uncertainties are handled. In the velocity loop, the performance guaranteed adaptive control is enhanced with the bound estimation mechanism, which accommodates modeling parametric uncertainties with a low dynamic order. The effectiveness of the proposed control is verified via simulations.

Understanding take-over performance of high crash risk drivers during conditionally automated driving

Understanding driver behavior of conditionally automated driving is necessary to ensure a safe transition from automated to manual driving. This study aimed to examine the difference in take-over performance between high crash risk (HCR) and lower crash risk (LCR) drivers in emergency take-over situations during conditionally automated driving. In the current simulator study, a 3 × 3 (within-subjects) factorial design was used, including the task factors (no task, reading the news, and watching a video) and time budget factors (time budget = 3 s, 4 s, and 5 s). Forty-eight participants completed a test drive on an approximately 10 km long two-way six-lane urban road. The participants firstly were in manual control and then switched to the automated driving mode at a speed of 50 km/h. The automated driving system was able to detect a broken car in the ego-lane and requested the driver to take over the control of the vehicle. There are at least one or two other vehicles or motorcycles on each side of the ego-vehicle, resulting in fewer escape paths. For the two non-handheld non-driving-related tasks (NDRTs), the participants were asked to be fully engaged in a task without any need to monitor the road environments. Each participant completed nine emergency take-over situations. The participants were classified into two groups that were labeled LCR (N ≤ 2) and HCR drivers (N ≥ 3) according to the number of accidents per driver. The results show that LCR drivers had shorter brake reaction time compared to HCR drivers. For all drivers, the engagement in a task led to longer response times, and the time budget affected the longitudinal vehicle control. In addition, the task affected the response times for LCR and HCR drivers, but only the time budget affected the longitudinal vehicle control for LCR drivers. For all drivers, LCR and HCR drivers, the time budget and task affected the safety of take-over. Especially, the two non-handheld everyday tasks seem to have a similar effect on the drivers’ workload. Therefore, the HCR drivers had a lower hazard perception compared to the LCR drivers, and the factor regarding the individual difference of driving ability in take-over situations should be considered to design safe take-over concepts for automated vehicles.

MPC-based switched driving model for human vehicle co-piloting considering human factors

In vehicle longitudinal control, improved comfort and reduced operation workload for human drivers are achieved with the ACC (Adaptive Cruise Control), which still requires the driver to maintain full attention on monitoring, meanwhile, the risk of distraction and fatigue rises resulting from the long-term supervising task, which has a strong impact on the successful takeover. The key point to make full use of ACC advantages and make up human’s weakness on supervising task is the reasonable arrangement of control time of ACC and human driver. In this paper, an MPC (Model Predictive Control) based optimized switching strategy of longitudinal driving authority transition is proposed, which aims to provide proper advice for human drivers to handover and takeover control authority, through minimizing the overall index consisted of the operation workload, fuel consumption, takeover risk and tracking errors. In addition, a new driver longitudinal model considering actual reaction time delay and insensitive distance perception of human drivers is proposed as well, which combines with ACC to constitute a typical switched control system called the switched driving model as the predictive model for MPC. The stable condition of the new driver longitudinal model is derived by using describing function method and a sufficient condition to ensure steady switched system is given by using the Lyapunov method and LMI approach. The results of simulator experiments show that the new driver model describes the car-following behavior of real human driver better. What’s more, the simulation results demonstrate that the performance index of switched driving is smaller than the human driving and ACC driving only, and the optimization time is short enough to meet the requirement of engineering practice.

Quasi-translations for fast hybrid nonlinear model predictive control

We present the quasi-translation (QT) strategy for mixed-integer model predictive control (MPC) problems. This strategy handles the complexity introduced by discrete controls in a simple manner that takes advantage of the sequential nature of MPC optimization problems. Some general criteria is provided guiding their application. The QT strategy can be used to balance controller performance, chatter and turnaround time. We also introduce an approach to handle hybrid models with state-dependent switches for use with a symbolic formulation of the MPC problem that integrates seamlessly with the QT strategy.We investigate the QT strategy for a variety of hybrid nonlinear MPC problems including the classic benchmark Lotka–Volterra fishing problem, a diesel airpath control problem and longitudinal vehicle control. Results show the QT strategy consistently yields a reduction in turnaround times with excellent or minimally degraded performance as compared to competing strategies.

Heavy-duty vehicle longitudinal automation with hydraulic retarder via H infinity control and off-policy reinforcement learning

A novel hierarchical heavy-duty vehicle (HDV) longitudinal control strategy with a hydraulic retarder is proposed in this paper to achieve the HDV down-hill longitudinal automation in the deceleration process. The upper level controller generates the optimal desired retarder torque through the H infinity control and the off-policy reinforcement learning (RL), in which the H infinity control is able to attenuate those disturbances and the off-policy RL can solve the H infinity control with completely unknown system dynamics. Then, according to the optimal desired retarder torque, the lower level controller can calculate the desired control pressure for the retarder to control the HDV. The effectiveness of this HDV longitudinal control strategy is verified by simulations based on an experimentally verified retarder model. Compared to the sliding-mode-control (SMC) based controller, the simulation result shows the proposed control strategy has better capability to attenuate the disturbances and guarantee the longitudinal speed tracking performance.

What’s in a name? Drivers’ perceptions of the use of five SAE Level 2 driving automation systems

Introduction: Automobile manufacturers are developing increasingly sophisticated driving automation systems. Currently, the highest level of automation available on the market is SAE Level 2, which provides sustained assistance for both lateral and longitudinal vehicle control. The purpose of this study was to evaluate how drivers’ perceptions of what behaviors secondary to driving are safe while a Level 2 system is operating vary by system name. Methods: A nationally representative telephone survey of 2005 drivers was conducted in 2018 with questions about behaviors respondents perceived as safe while a Level 2 driving automation system is in operation. Each respondent was asked about two out of five system names at random for a balanced study design. Results: The name “Autopilot” was associated with the highest likelihood that drivers believed a behavior was safe while in operation, for every behavior measured. There was less variation observed among the other four SAE Level 2 system names when compared with each other. A limited proportion of drivers had experience with advanced driver assistance systems and fewer of these reported driving a vehicle in which Level 2 systems were available. Drivers reported that they would consult a variety of sources for information on how to use a Level 2 system. Conclusions: The names of SAE Level 2 driving automation systems influence drivers’ perceptions of how to use them, and the name “Autopilot” was associated with the strongest effect. While a name alone cannot properly instruct drivers on how to use a system, it is a piece of information and must be considered so that drivers are not misled about the correct usage of these systems. Practical Applications: Manufacturers, suppliers, and organizations regulating or evaluating SAE Level 2 automated driving systems should ensure that systems are named so as not to mislead drivers about their safe use.