Longitudinal Vehicle Control Research Articles

Vehicle Longitudinal Control with the Sliding Mode Method Considering Uncertainty of the Model

Abstract: In this paper, the control algorithm for longitudinal control of a passenger car that is moving on the highway and urban traffic is provided. Longitudinal dynamic equation including resisting forces acting on the vehicle presented and structural and non-structural uncertainties in the model are investigated. To control the speed of vehicles on the highway and vehicle distance control in city traffic, vehicle longitudinal control laws have been proposed. To control the speed of vehicles on the highway and in city traffic, vehicle longitudinal control laws have been proposed. In order to control the nonlinear systems, the sliding mode method which is a robust control method has been used for the design controller. The controller consists of two algorithms as speed control and distance control that their duty is done with the force generation to move the vehicle and overcome the disturbances and uncertainties using tracing of it by car. Finally, to ensure the ability of control and efficiency under different conditions, the control rules are applied to the vehicle dynamics equation.

A Hybrid Deep Reinforcement Learning and Optimal Control Architecture for Autonomous Highway Driving

Autonomous vehicles in highway driving scenarios are expected to become a reality in the next few years. Decision-making and motion planning algorithms, which allow autonomous vehicles to predict and tackle unpredictable road traffic situations, play a crucial role. Indeed, finding the optimal driving decision in all the different driving scenarios is a challenging task due to the large and complex variability of highway traffic scenarios. In this context, the aim of this work is to design an effective hybrid two-layer path planning architecture that, by exploiting the powerful tools offered by the emerging Deep Reinforcement Learning (DRL) in combination with model-based approaches, lets the autonomous vehicles properly behave in different highway traffic conditions and, accordingly, to determine the lateral and longitudinal control commands. Specifically, the DRL-based high-level planner is responsible for training the vehicle to choose tactical behaviors according to the surrounding environment, while the low-level control converts these choices into the lateral and longitudinal vehicle control actions to be imposed through an optimization problem based on Nonlinear Model Predictive Control (NMPC) approach, thus enforcing continuous constraints. The effectiveness of the proposed hierarchical architecture is hence evaluated via an integrated vehicular platform that combines the MATLAB environment with the SUMO (Simulation of Urban MObility) traffic simulator. The exhaustive simulation analysis, carried out on different non-trivial highway traffic scenarios, confirms the capability of the proposed strategy in driving the autonomous vehicles in different traffic scenarios.

Terminal Constraint-Free Model Predictive Longitudinal Control for Unmanned Ground Vehicles With Driving Force Table

In this paper, a hierarchical architecture is presented on the longitudinal control of unmanned ground vehicles (UGVs). A terminal constraint-free model predictive control (MPC) algorithm is developed as the upper controller to calculate the desired acceleration. For the lower controller, through modeling experiments, we establish a driving force table (DFT) to represent the relationship among throttle opening, speed and driving force of UGVs. Moreover, in order to improve the disturbance rejection capability, a DFT-based feedforward controller is proposed to convert the desired acceleration into the expected throttle opening. The modeling errors and external disturbances are integrated and an extended state observer (ESO) is designed for estimation. Besides, the recursive feasibility and stability of the MPC without terminal constraint are guaranteed through rigorous proof. Several experiments are conducted to show the effectiveness of the proposed strategy.

Model predictive control policy design, solutions, and stability analysis for longitudinal vehicle control considering shockwave damping

Longitudinal vehicle control models, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), have been the core component of many Automated Driving Systems (ADS) or Advanced Driver Assistance System (ADAS). ACC and CACC systems make vehicles drive with faster reaction time and smaller following distance than manual vehicles, alleviating the congestions and attenuating traffic disturbances. Many existing ACC and CACC control models focus on three strategies that can indirectly mitigate congestions and damp traffic shockwaves: time headway suppression, stability improvement, and adaptive mode-switching control based on traffic conditions. Although congestion mitigation and shockwave damping are achievable with those control strategies, the improvement is often limited to the by-product effect of their main control objectives of headway suppression and system stability. Many adaptive models also need to calibrate many parameters for different road conditions.This paper proposes a new ACC and CACC framework that directly integrates the congestion shockwave damping and traffic congestion mitigation into the objective function of a dynamic control framework. A Model Predictive Control (MPC) based dynamic optimization framework is proposed to balance the trade-offs among multiple objectives, including safety, efficiency, shockwave, elasticity, and driver’s comfort. The proposed framework is applied in both ACC and CACC platoons mixed with manual vehicles, and five different control models are explored. The stability conditions of the proposed models are derived analytically and analyzed numerically. Three experimental studies, including platoon, ring-road, and traffic simulation studies, indicate promising results of proposed models in reducing shockwave propagation speed and mitigating traffic congestion under different environments compared with existing ACC and CACC models.

Empirical study of a cooperative longitudinal control for merging maneuvers considering courtesy and mixed autonomy

This study focuses on how to improve the merge control prior to lane reduction points due to either accidents or constructions. A Cooperative longitudinal Control for Merging maneuvers (CCM) strategy based on Automated Vehicles (AV) is proposed considering cooperation among vehicles, courtesy, and the coexistence of AV and Human-Driven Vehicles (HDV). CCM introduces a modified/generalized Cooperative Adaptive Cruise Control (CACC) for vehicle longitudinal control prior to lane reduction points. It also takes courtesy into account to ensure that AV behave responsibly and ethically. CCM is evaluated using microscopic traffic simulation and compared with no control and CACC merge strategies. The results show that CCM consistently generates the lowest delays and highest throughputs approaching the theoretical capacity. Its safety benefits are also found to be significant based on vehicle trajectories and density maps. CCM mainly requires vehicles to have automated longitudinal (such as Adaptive Cruise Control (ACC)) and lane-changing control, which are already commercially available on some vehicles. Also, it does not need 100% AV penetration, presenting itself as a promising solution for improving traffic operations in lane reduction transition areas such as highway work zones.

Research on Longitudinal Control Algorithm of Adaptive Cruise Control System for Pure Electric Vehicles

The vehicle longitudinal control algorithm is the core function of the adaptive cruise control system, whose main task is to convert vehicle acceleration and deceleration requirements into vehicle driving and braking commands so that the vehicle can quickly and accurately track the desired acceleration. Traditional longitudinal control algorithms rely on accurate vehicle dynamic modeling or complex controller parameter calibrations. To overcome those difficulties, a longitudinal control algorithm based on RBF-PID is proposed in this paper. The algorithm uses the RBFNN (radial basis function neural network), which can simply and quickly approximate any complex nonlinear system, to identify the Jacobian information of the vehicle and perform parameter tuning for PID control and achieve vehicle longitudinal control with self-tuning capability. Finally, the algorithm of this paper is verified by the joint simulation of Matlab/Simulink and Carsim. The results show that this algorithm has a better response rate and anti-jamming capability than the traditional PID control and can achieve accurate and rapid tracking of the desired acceleration.

Explicitly incorporating surrogate safety measures into connected and automated vehicle longitudinal control objectives for enhancing platoon safety

The concepts of Connected and Automated Vehicles (CAV) and vehicle platooning have generated high expectations regarding the safety performance of future transportation systems. Existing CAV longitudinal control research primarily focuses on efficiency and control stability, by considering different inter-vehicle spacing policies. In very few cases, safety was also considered as a constraint, but not in the main control objectives. Theoretically, stability can only guarantee that CAV platoons eventually achieve an equilibrium state but is unable to promise safety along the process of achieving equilibrium. It is important to note that CAV does not mean absolutely safe, and its longitudinal or platoon control safety performance depends on how the control algorithms are designed, how accurately it can detect and predict its lead vehicle’s (could be a human-driven vehicle) next move, and other practical factors such as control and communication delays. To optimize CAV platoon safety, this study integrates surrogate safety measures (SSM) and model predictive control (MPC) into CAV longitudinal control for trajectory optimization. SSM has been widely adopted for modeling the safety consequences of various vehicle control strategies and identifying near-crash events from either simulated or field-captured traffic data. This study directly incorporates three typical SSM into the longitudinal control objectives of CAV and constructs a state-space MPC algorithm to model how these SSM vary as a result of CAV dynamics. Numerical examples are provided to show the performance of these SSM-based optimal CAV longitudinal control methods under traffic flow perturbations. To further confirm the necessity of explicitly considering SSM in CAV longitudinal control and its effectiveness in reducing rear-end collision risk, the proposed methods are compared with three classical longitudinal control models that do not consider SSM based on microscopic traffic simulation. It is noted that all SSM-based optimal control methods perform better than others as manifested by some key risk indicators, demonstrating the importance of explicitly considering SSM and safety in CAV longitudinal control.

What Is the Best Way to Optimally Parameterize the MPC Cost Function for Vehicle Guidance?

Model predictive control (MPC) is a promising approach to the lateral and longitudinal control of autonomous vehicles. However, the parameterization of the MPC with respect to high-level requirements such as passenger comfort, as well as lateral and longitudinal tracking, is challenging. Numerous tuning parameters and conflicting requirements need to be considered. In this paper, we formulate the MPC tuning task as a multi-objective optimization problem. Its solution is demanding for two reasons: First, MPC-parameterizations are evaluated in a computationally expensive simulation environment. As a result, the optimization algorithm needs to be as sample-efficient as possible. Second, for some poor parameterizations, the simulation cannot be completed; therefore, useful objective function values are not available (for instance, learning with crash constraints). In this work, we compare the sample efficiency of multi-objective particle swarm optimization (MOPSO), a genetic algorithm (NSGA-II), and multiple versions of Bayesian optimization (BO). We extend BO by introducing an adaptive batch size to limit the computational overhead. In addition, we devise a method to deal with crash constraints. The results show that BO works best for a small budget, NSGA-II is best for medium budgets, and none of the evaluated optimizers are superior to random search for large budgets. Both proposed BO extensions are, therefore, shown to be beneficial.

Effect of emotion on galvanic skin response and vehicle control data during simulated driving

PurposeThis study aims to investigate the effect of driver emotion on the physiological and vehicle control data and the possibility of predicting the driver emotion to enhance the driving experience. MethodThe driver emotions in the driving context were classified into eight categories depending on high and low levels of arousal and valence: happiness, surprise, fear, anger, depression, boredom, relief, and neutrality. Fourteen male and female volunteers with ages between 22 and 34 y participated in the test, and approximately 540 min of image, physiological, and vehicle data were collected. After inducing the participants’ target emotion through the viewing of a film and writing of passages, we asked the participants to drive on a highway through a driving simulator and self-evaluate their emotion. After the test, the participants were allowed to go home after emotion neutralization. ResultThe participants’ self-evaluated emotions correlated highly with the intended induced emotions. High arousal and negative valence emotions such as anger and low arousal and positive valence emotions such as relief exhibited a statistically significant elevation for the following indicators: galvanic skin response amplitude, longitudinal vehicle control data such as throttling and braking, and lateral vehicle control data. ConclusionsThe test results confirmed that a driver’s emotional state could be reflected in the differences between the biometric data or vehicle control data. In particular, the emotion associated with a high arousal and negative valence could be clearly distinguished from that associated with a low arousal and positive valence. Therefore, the driver's emotional state affects the traffic condition, and the detection of potentially risky emotions such as those associated with road rage and development of a suitable driving mode can help enhance the driving safety. ApplicationsDrivers’ emotions can be identified based on physiological data and vehicle control data and integrated into the system to formulate appropriate responses. For instance, anger anticipated from a driver can be alleviated by a preemptive measure, thereby enhancing the traffic safety.

Longitudinal control of an over-actuated off-road vehicle with fault tolerance and longitudinal slip consideration

A new fault-tolerant longitudinal control allocation is developed in this paper in order to distribute the different torques considering that the vehicle involved is an electrical four wheel driving vehicle. Its purpose is to keep an acceleration set-point given by the driver and to reckon with the wheels slip that can occur in an off-road terrain. The method is based on a constrained dynamic pseudo-inverse allocation.

Driving Behaviour and Usability: Should In-Vehicle Speed Limit Warnings Be Paired with Overhead Gantry?

Variable speed limits (VSL) aim at improving safety and traffic fluidity by increasing drivers’ awareness. In the present simulator study, VSL displayed on overhead gantries on a motorway were also displayed on a mobile phone, fixed on the vehicle’s centre console, with distance-based triggers (250 m vs. 500 m from the overhead gantry). Results showed drivers (N = 20) complied with the in-vehicle information, which was congruent with the upcoming gantry. The sooner the in-vehicle VSL, the faster the speed when speed limits increased. Similarly, the sooner the in-vehicle VSL, the slower the speed when speed limits decreased. Later in-vehicle VSL resulted in lower speed homogeneity, which is a safety concern. Speed homogeneity was greater when no in-vehicle VSL were displayed. Finally, the 70 mph VSL were affecting driving behaviour differently. These results suggested that there might be traffic disruption and more erratic longitudinal vehicle control on real roads.

Efficient Anticipatory Longitudinal Control of Electric Vehicles through Machine Learning-Based Prediction of Vehicle Speeds

Driving style and external factors such as traffic density have a significant influence on the vehicle energy demand especially in city driving. A longitudinal control approach for intelligent, connected vehicles in urban areas is proposed in this article to improve the efficiency of automated driving. The control approach incorporates information from Vehicle-2-Everything communication to anticipate the behavior of leading vehicles and to adapt the longitudinal control of the vehicle accordingly. A supervised learning approach is derived to train a neural prediction model based on a recurrent neural network for the speed trajectories of the ego and leading vehicles. For the development, analysis and evaluation of the proposed control approach, a co-simulation environment is presented that combines a generic vehicle model with a microscopic traffic simulation. This allows for the simulation of vehicles with different powertrains in complex urban traffic environment. The investigation shows that using V2X information improves the prediction of vehicle speeds significantly. The control approach can make use of this prediction to achieve a more anticipatory driving in urban areas which can reduce the energy consumption compared to a conventional Adaptive Cruise Control approach.

Trajectory Prediction Using Graph-Based Deep Learning for Longitudinal Control of Autonomous Vehicles: A Proactive Approach for Autonomous Driving in Urban Dynamic Traffic Environments

This article presents a method for the trajectory prediction of surrounding vehicles and proactive longitudinal control of autonomous vehicles (AVs) in an urban road environment. A long short-term memory (LSTM)-based deep learning model is designed for the surrounding vehicles’ trajectory prediction. In our model, the historical evolution of the relation between a target vehicle and lanes is considered to learn the driver’s behavior in a lane-aware manner. Interaction among adjacent vehicles is captured based on a graph convolutional network (GCN), which uses a self-attention mechanism. Compared to other approaches, our prediction model utilizes environment information that is acquirable in AVs with local sensors. A model predictive control (MPC) is designed to derive the control inputs of acceleration for AVs. The proposed control method utilizes the prediction results of the target vehicle to give action requests to AV in a proactive manner considering both safety and ride quality. The results of comparative studies indicate that the proposed prediction model achieves improved accuracy compared to baselines. The control results provided by automated driving tests show that the proposed control algorithm applied by the LSTM-based prediction model enables AVs to achieve safety with respect to surrounding vehicles and provide ride comfort to passengers.

Research on tyre mechanics model for multi-axle coordinated steering of heavy vehicles

Compared with passenger cars, heavy vehicles have larger loads, longer bodies, more steering modes, and are more likely to cause improper steering angle relationships among the wheels, resulting in uncoordinated steering. This uncoordinated steering makes the tyres roll and drag, which results in abnormal lateral and longitudinal slips, changing the lateral and longitudinal mechanical characteristics of tyres and leading to low path tracking accuracy and poor steering performance of vehicles. Therefore, a tyre model containing lateral and longitudinal mechanical characteristics under heavy load is the key to solving the problem of uncoordinated steering in heavy vehicles. Firstly, with the physical tyre model, the lateral and longitudinal slip is described as carcass deflections. With the carcass deflections and contact pressure distribution of heavy load, the tyre forces under uncoordinated steering conditions are derived. Then, the tyre imprint parameters are obtained with a test bench, and a two-dimensional contact pressure distribution model is established to reproduce the contact pressure distribution of tyre under heavy loads and dynamic slips. The comparison results with Magic Formula (MF) and Trucksim are generally consistent, proving the effectiveness of the model. Finally, a lateral and longitudinal tyre force distribution control of vehicles is carried out based on the established model. The results of the Trucksim-Simulink co-simulation show that the path tracking accuracy of vehicles is significantly improved, which demonstrates that the tyre model incorporating lateral and longitudinal mechanical characteristics under heavy loads can provide a theoretical basis for solving the problem of uncoordinated steering in heavy vehicles.

Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms.

The age of algorithms is here, and it is really changing people's lives. More and more ethical problems related to algorithms have attracted people's attention, but the related ethical research is still far behind the research of algorithms. As more intelligent algorithms emerge in an endless stream, there will also be a lot of algorithmic ethical issues. On the other hand, with the continuous improvement of the development level of the automobile industry, people have a stronger demand for the safety and stability of modern transportation, and more and more autonomous driving technology has been promoted and applied in the market. At present, most of the studies on the longitudinal collision avoidance system of vehicles use collision warning or emergency braking to avoid collision. However, when the vehicle is in a special situation such as high speed and slippery road, emergency steering is more effective. In order to further improve the vehicle safety and ethical algorithm design points, this article revolves around vehicle lateral active collision avoidance control method research, the collision avoidance decision-making, and path planning and collision avoidance transverse vehicle longitudinal motion control is analyzed, and based on automated driving simulation experiment, the tests carried out to verify the designed control strategy. The experimental results show that the proposed method not only has a good effect of preventing automatic driving collision but also can meet the requirements of algorithm ethics. This research can effectively guide the research of algorithmic ethics in the field of autonomous driving and effectively reduce the occurrence of traffic accidents.

Impact of battery cell imbalance on electric vehicle range

Due to manufacturing variation, battery cells often possess heterogeneous characteristics, leading to battery state-of-charge variation in real-time. Since the lowest cell state-of-charge determines the useful life of battery pack, such variation can negatively impact the battery performance and electric vehicles range. Existing research has been focused on control design to mitigate cell imbalance. However, it is yet unclear how much impacts the cell imbalance can have on electric vehicle range. This paper closes this knowledge gap by using a simulation environment consisting of real-world driving speed data, vehicle longitudinal control, propulsion and vehicle dynamics, and cell level battery modeling. In particular, each battery cell is modeled as an equivalent circuit model, and variations among cell parameters are introduced to assess their impact on electric vehicles range and to identify the most influential parameter variations. Simulation results and analysis can be used to assist balancing control design and to benchmark control performance.

Two-Dimensional Car-Following Control Strategy for Electric Vehicle Based on MPC and DQN

For the coupling problem of longitudinal control and lateral control of vehicles, a two-dimensional (2-D) car-following control strategy for an electric vehicle is proposed in this paper. First, a 2-D car-following model for longitudinal following and lateral lane keeping is established. Then, a 2-D car-following control strategy is designed, and the longitudinal following control and lateral lane keeping control are integrated into one model predictive control (MPC) framework. The 2-D car-following strategy can realize the multi-objective coordinated optimization for longitudinal control and lateral control during the 2-D car-following process, and the multiple objectives are: safety, tracking, comfort, lane keeping, lateral stability and economy. In addition, the economy is important for electric vehicles. The weight matrix of the objective function in the MPC framework is symmetric, and the weight coefficients for the weight matrix have a great influence on the control. The contribution of this paper is: in order to adapt to different dynamic processes of lane keeping, the weight coefficients in the MPC framework are optimized in real-time based on the deep Q network (DQN) algorithm. Finally, to verify the 2-D car-following control strategy, a comparison strategy and two experimental scenarios are set, and simulation experiments are carried out. In scenario 1, compared with the comparison strategy, the lane keeping, lateral stability and economy of the proposed strategy are improved by 37.21%, 17.57% and 9.26%, respectively. In scenario 2, compared with the comparison strategy, the lane keeping, lateral stability and economy of the proposed strategy are improved by 36.45%, 16.66% and 18.52%, respectively. Therefore, compared with the comparison strategy, the 2-D car-following control strategy can have better lane keeping, lateral stability and economy on the premise of ensuring other performances during the 2-D car-following process.

A Parallelized Self-Driving Vehicle Controller Using Unsupervised Machine Learning

In this article, a self-driving vehicle controller that optimizes the path a vehicle follows from its initial position to its destination is presented. The methods include clustering-based k-means, hierarchical, Gaussian matrix model, and self-organizing mapping. The real-time parallel implementation of the unsupervised machine learning algorithms could provide fast response times of under one microsecond during the lateral, longitudinal, and angular motion control of the autonomous vehicle. It was observed that a random selection of one of the machine learning methods may not always guarantee the optimality of the position and velocity variables as compared to the desired values. The proposed parallel implementation and optimization of the algorithms could have a significant contribution towards making transportation mobility more reliable and sustainable for future vehicular systems.

Model-free adaptive sliding mode control for intelligent vehicle longitudinal dynamics

Vehicle longitudinal dynamics system has the characteristics of being strongly non-linear, time-varying, and multiple-perturbed, so, it is difficult to build the mathematical model accurately. The control algorithms, based on accurate mathematical model, can hardly achieve the ideal effect, but control methods, which merely adopt input/output data (I/O) of a system, provides a solution. In this paper, by means of combing model-free adaptive control (MFAC) and sliding-mode control (SMC), the model-free adaptive sliding mode control (MFASMC) method is proposed. By comparison with feedback-feedforward control method, the MFASMC method can better improve the control effect and anti-disturbance performance. Meanwhile, the stability of MFASMC method was proven mathematically. Besides, the parameters of MFASMC method were optimized using genetic algorithm. Results of simulation and HiL test shows that the MFASMC method has fast response, strong robustness and smooth output. It would be better to apply it to the longitudinal dynamics control of intelligent vehicles.

Do adaptive cruise control and lane keeping systems make the longitudinal vehicle control safer? Insights into speeding and time gaps shorter than one second from a naturalistic driving study with SAE Level 2 automation

Advanced driver assistance systems such as adaptive cruise control (ACC) and lane keeping system (LKS) potentially contribute to reducing crash rates and traffic congestion. On-road studies based on early ACC systems operational at medium–high speeds only have shown that the system reduces the proportion of short time gaps when activated. Despite the effects on driver behaviour, most mathematical models assessing the impact of ACC and LKS systems on crash rates and traffic congestion are not based on empirical findings.This study examines the factors that influence changes in the longitudinal vehicle control when driving with ACC and LKS. The data were collected in a naturalistic driving experiment with full-range ACC and LKS and two different vehicle brands (BMW and Tesla) in the Netherlands. To capture changes that are relevant for traffic safety, speeding and a time gap shorter than one second were investigated. The factors influencing speeding and short time gaps were analysed using statistical tests and logistic regression models with random effects, that allow to control for the impact of different explanatory variables and correlations between repeated 10-s intervals over time.The findings revealed that, overall, drivers were less likely to speed and they were also less likely to have a time gap shorter than one second in the experimental condition with the ACC and the LKS than in the baseline condition in manual driving. Drivers were likely to speed in the following 10-s interval when the current speed was close to the speed limit, and/or when the next speed limit was lower than the current speed limit, and/or when the acceleration was high. Drivers were likely to have a short time gap in the following 10-s interval when approaching a slower leader, and/or when the current time gap was short and/or when the acceleration was high. Controlled for these main factors, drivers were less likely to speed and to have a short time gap when the ACC and the LKS were active. However, drivers were more likely to speed when overruling the ACC by pressing the gas pedal. When the systems were active, one vehicle brand showed a smaller probability of a short time gap than the other brand, suggesting differences in ACC system settings between brands. In addition, the speeding probability increased while the probability of a short time gap decreased over time during the trip after the activation of the systems. Although further studies including a larger sample of participants and a wider range of traffic situations are needed, the results are useful to the design of automated vehicles that prevent speeding and short time gaps, and to the implementation of traffic simulations that evaluate the impact of ACC and LKS on crash rates and traffic congestion according to realistic on-road data.