Longitudinal Vehicle Research Articles

Infrastructure-Assisted cooperative driving and intersection management in mixed traffic conditions

Infrastructure-assisted cooperative driving, which consists of connected and automated vehicle (CAV) trajectory planning and traffic signal control, can greatly improve the efficiency of signalized intersection operations. In existing studies, the CAV trajectory planning problem is simplified to only modeling longitudinal vehicle behaviors or assuming the lane changing can be executed instantaneously. The generated CAV trajectories are not realistic and can’t be implemented at the vehicle level. Instantaneous lane changing process may also lead to safety issues or uncomfortable driving behavior of following vehicles in a mixed traffic environment. To fill this research gap, we propose an optimization framework that integrates a full CAV trajectory planning model with traffic signal control in a cooperative driving environment with both CAVs and human-driven vehicles (HDVs). A two-level optimization model is formulated based on discrete time. The high-level model optimizes traffic signal parameters, CAV arrival time, and lane assignment at the stop bar, to minimize total vehicle delay. Given arrival time and lane assignment, the low-level model generates trajectories with integrated car-following and lane-changing maneuvers to mimic a human driving policy based on imitation learning. Numerical experiments from a real-world intersection show that the proposed model outperforms adaptive and fixed time signal control by as much as 63.06% in terms of reduction of average vehicle delay, due to better utilization of lane capacity and reduced lost time. In addition to mobility benefits, fuel consumption is also significantly reduced under the proposed infrastructure-assisted cooperative driving framework.

A Comparative Study of Longitudinal Vehicle Control Systems in Vehicle-to-Infrastructure Connected Corridor

<div>Vehicle-to-infrastructure (V2I) connectivity technology presents the opportunity for vehicles to perform autonomous longitudinal control to navigate safely and efficiently through sequences of V2I-enabled intersections, known as connected corridors. Existing research has proposed several control systems to navigate these corridors while minimizing energy consumption and travel time. This article analyzes and compares the simulated performance of three different autonomous navigation systems in connected corridors: a V2I-informed constant acceleration kinematic controller (V2I-K), a V2I-informed model predictive controller (V2I-MPC), and a V2I-informed reinforcement learning (V2I-RL) agent. A rules-based controller that does not use V2I information is implemented to simulate a human driver and is used as a baseline. The performance metrics analyzed are net energy consumption, travel time, and root-mean-square (RMS) acceleration. Two connected corridor scenarios are created to evaluate these metrics, including one scenario reconstructed from real-world traffic signal data. A sensitivity analysis is also performed to quantitatively identify key parameters that have the highest impact on the three metrics of interest.</div>

Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm

Most existing ESC (electronic stability control) and ADS (auto drive system) stability controls rely on the measurement of yaw rate and sideslip angle. However, the existing sensors are too expensive, which is one of the factors that makes it difficult to measure the side slip angle of vehicles directly. Therefore, the estimation of sideslip angle has been extensively discussed in the relevant literature. Accurate modeling is complicated by the fact that vehicles are highly nonlinear. This article combines a radial basis function neural network with an unscented Kalman filter to propose a new sideslip angle estimation method for controlling the dynamic behavior of vehicles. Considering the influence of input data type and sensor ease of measurement factors on the results, a two-degrees-of-freedom vehicle nonlinear dynamic model was established, and a radial basis function neural network estimation algorithm was designed. In order to reduce the impact of noise and improve the reliability of the algorithm, the neural network algorithm was combined with the Kalman filter. The information collected from low-cost sensors for actual vehicle operation (longitudinal vehicle speed, steering wheel angle, yaw rate, lateral acceleration) was trained using a radial basis function neural network to obtain a “pseudo slip angle”. The “pseudo slip angle”, yaw rate, and lateral acceleration are input as observations of the Kalman filter. The sideslip angle obtained from different observation methods was compared with the values provided by the Carsim 2020. The experiment shows that the sideslip angle estimator based on the radial basis function neural network and unscented Kalman filter achieves the optimal effect.

Coupled nonlinear controller for vehicle trajectory tracking in a deformable soil: Application to a four-wheeled mobile agricultural robot

In this paper, the coupled longitudinal and lateral control of the mobile agricultural robot “Agri-Eco-Robot’’ is addressed. As a first step, the Newton's law is used to develop the dynamical modeling of the mobile agricultural robot. The wheel-ground contact is modeled using the Terramechanics law called Extended-Bekker. The validation of the developed vehicle model was then conducted using an automotive simulator. The developed vehicle model is then used to derive the coupled control laws for the lateral and the longitudinal vehicle dynamics. The proposed controller is realized using two overlapping controllers, the first is dealing with coupled control of longitudinal and lateral dynamics to command the traction, and the second is the controller that minimize slipping, both are developed using the Lyapunov's theory. This controller is compared with the dynamics where the slip ratio is not controlled according to two scenarios in which one is the heigh velocity and the other is low velocity, this control law is validated using an automotive simulator applied to the mobile robot ‘Agri-Eco-Robot’. The result of this control law shows the necessity of the slipping control when navigating in a rough environment such as agricultural fields, assuming a low-speed command to ensure system stability.

Carbon footprint comparison analysis of passenger car segment electric and ICE propelled vehicles in Kuwait

Considering the decrease in the fossil-fuel reserves and environmental drawbacks of internal combustion engine (ICE) propelled vehicles, the usage of alternative prime movers such as electric vehicles (EVs) has gained significant potential in the last decades. Battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs) are a few currently available examples of vehicle electrification. EVs have zero in-use emissions, however, depending on the region to be operated, well-to-wheel (WTW) emissions need to be considered for a realistic carbon footprint effect considering global warming (GW) analysis. Especially, considering the countries where the usage of renewable energy (RE) sources is extremely limited, such as Kuwait and other GCC countries, the benefit of EVs significantly decreases. Within this perspective in this study, an inclusive CO2 emissions analysis for two passenger segment vehicles with different prime movers are analyzed: ICE and electric motor (EM). Longitudinal vehicle models were developed via MATLAB/Simulink software for both prime mover configurations which are capable of simulating the fuel/energy consumption using Toyota Camry and Lexus UX 300e vehicles. The generated CO2 emissions by the two prime movers were compared using the previously generated Kuwait driving cycle (Kuwait DC). The results indicated that the use of BEVs generated fewer CO2 emissions nearly half the amount of what ICE propelled vehicles generated.

Particle-Based Optimization Algorithms for Longitudinal Control of Autonomous Vehicle: A Comparative Study

In order to improve the stability and performance of an autonomous vehicle, optimization needs to be explicitly performed in the controllers, which has an essential part in the tracking system. This work proposes a novel longitudinal control optimization scheme and a novel longitudinal controller consisting of a feed-forward and feedback term. The feed-forward term is inspired by the vehicle’s steady-state response, whereas the feedback term is a proportional-integral-derivative (PID) controller. Also, a model representing the longitudinal vehicle dynamics is designed based on physical phenomena affecting the vehicle. Besides, some nature-inspired optimization algorithms are used to determine the optimal model parameters and optimize the controller parameters, i.e., Particle Swarm Optimization (PSO), Accelerated PSO (APSO), Flower Pollination Algorithm (FPA), and Modified FPA (MFPA). The algorithms are compared in optimizing the longitudinal vehicle model and controller using the CARLA simulator, and stability tests are also done for each algorithm. In addition, the characteristics of several cost functions in controller optimization are inspected. The results show that the MFPA is the most stable algorithm, the proposed model represents the system satisfactorily, and optimizing the controller using a regularized cost function leads to better overall performance. Our code is available in https://github.com/fadamsyah/Particle-Based-Optimization-for-Longitudinal-Control.

Maximum Correntropy Square-Root Cubature Kalman Filter with State Estimation for Distributed Drive Electric Vehicles

For nonlinear systems, both the cubature Kalman filter (CKF) and square-root cubature Kalman filter (SCKF) can get good estimation performance under Gaussian noise. However, the actual driving environment noise mostly has non-Gaussian properties, leading to a significant reduction in robustness and accuracy for distributed vehicle state estimation. To address such problems, this paper uses the square-root cubature Kalman filter with the maximum correlation entropy criterion (MCSRCKF), establishing a seven degrees of freedom (7-DOF) nonlinear distributed vehicle dynamics model for accurately estimating longitudinal vehicle speed, lateral vehicle speed, yaw rate, and wheel rotation angular velocity using low-cost sensor signals. The co-simulation verification is verified by the CarSim/Simulink platform under double-lane change and serpentine conditions. Experimental results show that the MCSRCKF has high accuracy and enhanced robustness for distributed drive vehicle state estimation problems in real non-Gaussian noise environments.

Model checking embedded adaptive cruise controllers

While checking functional correctness for automated driving is often achieved through vast amounts of automated and manual field testing, automating specification verification is essential for developing and releasing fully self-driving vehicles. This paper presents an automatic bounded model checking approach for functional specifications over longitudinal vehicle controller implementations. The proposed method checks the actual embedded program, rather than an extracted abstract model. The specification is converted into a monitor that is interleaved with the execution of the program over a finite time horizon in closed-loop simulation. A decomposition is proposed to tackle the verification complexity. This allows verifying the program for whole parameter sets using a software model checker as opposed to testing only individual samples. The approach is capable of identifying functional flaws in several exemplary longitudinal controllers with variable complexity.

Joint Optimization of Platoon Control and Resource Scheduling in Cooperative Vehicle-Infrastructure System

Vehicle platooning technology is essential in achieving group consensus, on-road safety, and fuel-saving. Meanwhile, Vehicle-to-Infrastructure (V2I) communication significantly facilitates the development of connected vehicles. However, the coupled effects of the longitudinal vehicle's mobility, platoon control and V2I communication may result in a low reliable communication network between the platoon vehicle and the roadside unit, there is a tradeoff between the platoon control and communication reliability. In this paper, we investigate a bi-objective joint optimization problem where the first objective is to maximize the success probability of data transmission (communication reliability) and the second objective function is to minimize the traffic oscillation flow. The vehicle's mobility state of the platoon vehicle affects the channel capacity and transmission performance. In this context, we deeply explore the relationship between control signals and resource scheduling and theoretically deduce a closed-form expression of the optimal communication reliability objective. Through this closed expression, we transform the bi-objective model into a single objective MPC model by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -constraint method. We design an efficient algorithm for solving the joint optimization model and prove the convergence. To verify the effectiveness of the proposed method, we finally evaluate the spacing error, speed error, and resource scheduling of platooning vehicles through simulation experiments in two experimental scenarios. The results show that the proposed control-communication co-design can improve the platoon control performance while satisfying the high reliability of V2I communications.

Design and Evaluation of Lane-Change Collision Avoidance Systems in Semi-Automated Driving

Hands on the steering wheel partial driving automation, in which the system controls the lateral and longitudinal vehicle motion while the driver holds the steering wheel, monitors the roadway, and intervenes when necessary, is an example of shared control driving. To insure mutual safety in shared control driving, the system also needs to guide the driver's interventions to avoid hazardous actions, such as risky lane changes. This study proposes three steering interventions that activate automatically when the system detects a lane change by monitoring the steering wheel angle inputted by the driver and road section and determining that the two vehicles are on a collision course. A driving simulation experiment with 80 drivers was conducted to compare four conditions: 1) no steering intervention; 2) a haptic steering intervention that increases stiffness against steering toward the lane where a collision may occur; 3) an automatic steering intervention that decouples the driver's steering input and autonomously steered the vehicle away from the hazard; and 4) multiple steering interventions that generate different degrees of haptic and automatic steering interventions based on the time headway and time-to-collision between vehicles. Analysis of driving performance and safety under conditions with and without steering interventions indicate that all participants, at some points, initiated lane changes that are likely to result in a crash during partial driving automation. However, the three interventions effectively reduced lane-change collisions compared to the baseline. While the automatic steering intervention avoided all collisions, the multiple steering interventions were determined to provide sufficient safety while being more thoroughly accepted by the drivers than the haptic and automatic interventions separately. These findings have implications for developing adaptive collision avoidance systems considering user preference and driving performance.

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.

Competitors-Aware Stochastic Lap Strategy Optimisation for Race Hybrid Vehicles

World Endurance Championship (WEC) racing events are characterised by a relevant performance gap among competitors. The fastest vehicles category, consisting in hybrid vehicles, has to respect energy usage constraints set by the technical regulation. Considering absence of competitors, i.e. traffic conditions, the optimal energy usage strategy for lap time minimisation is typically computed through a constrained optimisation problem. To the best of our knowledge, the majority of state-of-the-art works neglects competitors. This leads to a mismatch with the real world, where traffic generates considerable time losses. To bridge this gap, we propose a new framework to offline compute optimal strategies for the powertrain energy management considering competitors. Through analysis of the available data from previous events, statistics on the sector times and overtaking probabilities are extracted to encode the competitors' behaviour. Adopting a multi-agent model, the statistics are then used to generate realistic Monte Carlo (MC) simulation of their position along the track. The simulator is then adopted to identify the optimal strategy as follows. We develop a longitudinal vehicle model for the ego-vehicle and implement an optimisation problem for lap time minimisation in absence of traffic, based on Genetic Algorithms. Solving the optimisation problem for a variety of constraints generates a set of candidate optimal strategies. Stochastic Dynamic Programming is finally implemented to choose the best strategy considering competitors, whose motion is generated by the MC simulator. Our approach, validated on data from a real stint of race, allows to significantly reduce the lap time.

Vertical-Longitudinal Coupling Effect Investigation and System Optimization for a Suspension-In-Wheel-Motor System in Electric Vehicle Applications

In-wheel-motor-drive electric vehicles have attracted enormous attention due to its potentials of improving vehicle performance and safety. Road surface roughness results in forced vibration of in-wheel-motor (IWM) and thus aggravates the unbalanced electric magnetic force (UEMF) between its rotor and stator. This can further compromise vertical and longitudinal vehicle dynamics. This paper presents a comprehensive study to reveal the coupled vertical–longitudinal effect on suspension-in-wheel-motor systems (SIWMS) along with a viable optimization procedure to improve ride comfort and handling performance. First, a UEMF model is established to analyze the mechanical–electrical–magnetic coupling relationship inside an IWM. Then a road–tire–ring force (RTR) model that can capture the transient tire–road contact patch and tire belt deformation is established to accurately describe the road–tire and tire–rotor forces. The UEMF and the RTRF model are incorporated into the quarter-SIWMS model to investigate the coupled vertical–longitudinal vehicle dynamics. Through simulation studies, a comprehensive evaluation system is put forward to quantitatively assess the effects during braking maneuvers under various road conditions. The key parameters of the SIWMS are optimized via a multi-optimization method to reduce the adverse impact of UEMF. Finally, the multi-optimization method is validated in a virtual prototype which contains a high-fidelity multi-body model. The results show that the longitudinal acceleration fluctuation rate and the slip ratio signal-to-noise ratio are reduced by 5.07% and 6.13%, respectively, while the UEMF in the vertical and longitudinal directions varies from 22.2% to 34.7%, respectively, and is reduced after optimization. Thus, the negative coupling effects of UEMF are minimized while improving the ride comfort and handling performance.

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.

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 Vehicle Motion Prediction in Urban Settings With Traffic Light Interaction

Predictive cruise control functions designed to reduce the energy consumption of intelligent and automated vehicles require an accurate prediction of the upcoming traffic situation in general and the preceding vehicle in particular. This article presents the implementation of prediction models which do not rely on explicit information of the entire preceding vehicle queue. Using this assumption, prediction algorithms based on Conditional Linear Gauss (CLG) models and Deep Neural Network (DNNs) were trained using real-world measurements in an urban setting. The training was conducted with 6.6 hours of driving data specifically collected for this study. The results show that both approaches can provide accurate estimates of a preceding vehicle’s motion. For the CLG models, a queue-estimation logic was implemented to improve the prediction accuracy. The most accurate prediction could be obtained when DNNs are set up with Long Short-Term Memory (LSTM) layers, capable of modeling time dependencies. The results also show that accurate short and mid term predictions of a vehicle’s trajectory can be made based primarily on sensor data, even when no or only little data from vehicle-to-vehicle (V2V) communication is available.

Coupled Lateral and Longitudinal Controller for Over-Actuated Vehicle in Evasive Maneuvering With Sliding Mode Control Strategy

Coupled controllers are vital for safely handling vehicles, especially in critical driving situations that include changing lanes to avoid obstacles. Controllers specialized in emergencies must keep road users safe in critical situations. In this paper, we develop the coupled controller to handle evasive maneuvers for an over-actuated vehicle. The controller is based on the second-order sliding mode control theory. We use the bicycle model to establish the equivalent and robust steering equations as a control-oriented model. The lateral and longitudinal vehicle motions are coupled to each other by the lateral vehicle information on the longitudinal sliding surface, and the dependence of the lateral sliding surface on the longitudinal velocity. The torque vectoring method based on fuzzy logic adjusts the yaw moment. We address the tire slip circle on the slip controller to stabilize the vehicle while maneuvering. We simulate and evaluate our controller in a rear-end collision situation with a short time window to maneuver the vehicle. The ego vehicle detects the preceding vehicle and performs an evasive lane change while simultaneously applying brakes to bring the vehicle to a halt. Our research is the earliest in providing an ultimate emergency control to successfully avoid crashes up to 130 km/h in short time crash detection.

Learning Based Longitudinal Vehicle Platooning Threat Detection, Identification and Mitigation

The security of cyber-physical systems, such as vehicle platoons, is critical to ensuring their proper operation and acceptance to society. In platooning, vehicles follow one another according to an agreed-upon control law that determines vehicle separation. We show that a vehicle within a platoon and under the control of a malicious actor could cause collisions or decrease the efficiency of surrounding vehicles. This paper focuses on detecting, identifying, and mitigating so-called destabilizing attacks that could cause vehicle collisions. Our approach is decentralized and requires only local sensor information for each vehicle to identify the vehicle responsible for the attack and then deploy an appropriate mitigating controller that prevents collisions. We use a Deep Learning approach (Convolutional Neural Network) with various data preprocessing techniques to detect and identify the malicious vehicle. Results indicate that even with decent noise levels in range and relative speed data, we achieve accuracy up to 96.3%. Also, once the adversarial vehicle is localized, we derive conditions for controller gains using the Routh Hurwitz criterion to mitigate the attack and ensure the stability of the platoon. Realistic simulator CARLA and MATLAB simulation results validate the effectiveness of our proposed approaches.

A Convex Optimal Control Framework for Autonomous Vehicle Intersection Crossing

Cooperative vehicle management emerges as a promising solution to improve road traffic safety and efficiency. This paper addresses the speed planning problem for connected and autonomous vehicles (CAVs) at an unsignalized intersection with consideration of turning maneuvers. The problem is approached by a hierarchical centralized coordination scheme that successively optimizes the crossing order and velocity trajectories of a group of vehicles so as to minimize their total energy consumption and travel time required to pass the intersection. For an accurate estimate of the energy consumption of each CAV, the vehicle modeling framework in this paper captures 1) friction losses that affect longitudinal vehicle dynamics, and 2) the powertrain of each CAV in line with a battery-electric architecture. It is shown that the underlying optimization problem subject to safety constraints for powertrain operation, cornering and collision avoidance, after convexification and relaxation in some aspects can be formulated as two second-order cone programs, which ensures a rapid solution search and a unique global optimum. Simulation case studies are provided showing the tightness of the convex relaxation bounds, the overall effectiveness of the proposed approach, and its advantages over a benchmark solution invoking the widely used first-in-first-out policy. The investigation of Pareto optimal solutions for the two objectives (travel time and energy consumption) highlights the importance of optimizing their trade-off, as small compromises in travel time could produce significant energy savings.

Security Enhancement for Longitudinal Vehicle Platooning Under Denial-of-Service Attacks: From Resilient Controller Design Perspective

Longitudinal vehicle platooning provides one of the critical ancillary services in autonomous intelligent transportations for maintaining the appropriate longitudinal distance with preceding vehicles. In view of the vulnerabilities of control module to underlying denial-of-service (DoS) attacks, this paper designs a resilient controller for longitudinal vehicle platooning to realize security enhancement. Firstly, the DoS attacks on communication links from sensors to controllers are depicted by random Bernoulli variables. Then, robust mean-square asymptotic stability criteria are derived for the composite system model, based on which the resilient controller gains are obtained. Finally, simulation results validate the effectiveness of the presented resilient controller under different DoS attack probabilities.