Lead Vehicle Research Articles

Young Novice Drivers' Cognitive Distraction Detection: Comparing Support Vector Machines and Random Forest Model of Vehicle Control Behavior.

The use of mobile phones has become one of the major threats to road safety, especially in young novice drivers. To avoid crashes induced by distraction, adaptive distraction mitigation systems have been developed that can determine how to detect a driver's distraction state. A driving simulator experiment was conducted in this paper to better explore the relationship between drivers' cognitive distractions and traffic safety, and to better analyze the mechanism of distracting effects on young drivers during the driving process. A total of 36 participants were recruited and asked to complete an n-back memory task while following the lead vehicle. Drivers' vehicle control behavior was collected, and an ANOVA was conducted on both lateral driving performance and longitudinal driving performance. Indicators from three aspects, i.e., lateral indicators only, longitudinal indicators only, and combined lateral and longitudinal indicators, were inputted into both SVM and random forest models, respectively. Results demonstrated that the SVM model with parameter optimization outperformed the random forest model in all aspects, among which the genetic algorithm had the best parameter optimization effect. For both lateral and longitudinal indicators, the identification effect of lateral indicators was better than that of longitudinal indicators, probably because drivers are more inclined to control the vehicle in lateral operation when they were cognitively distracted. Overall, the comprehensive model built in this paper can effectively identify the distracted state of drivers and provide theoretical support for control strategies of driving distraction.

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.

Modeling and stability analysis of car-following behavior for connected vehicles by considering driver characteristic

Research of car-following behavior is very important to alleviate traffic congestion and ensure traffic safety. To describe the car-following behavior of connected vehicles and restore the real driving environment, this paper presents an improved car-following model for connected vehicles by considering driver characteristic and speed information of multiple vehicles, which reflects the interaction between adjacent connected vehicles and the influence of driver characteristic on the stability of car-following. The linear stability analysis and nonlinear stability analysis prove that the improved model is effective and can promote the stability of traffic flow more than other models. In addition, the numerical simulations show that the new model performs better in eliminating disturbances than other models. Finally, the parameters of proposed model are calibrated based on NGSIM data. The results show that the new model performs well in car-following process, keeping a safe distance from the leader vehicle without sudden acceleration and deceleration. The new model also can predict the acceleration behavior of the leader vehicle after emergency brake. In summary, the model proposed in this paper can be used as active safety technology to prevent collision accidents, or as a user-defined function or model in the traffic simulation software, or as car-following strategy in driverless algorithms.

Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment

This study aims to generate test cases for scenario-based assessment of automated driving systems (ADS) when encountering a cut-out maneuver where the lead vehicle having changed lanes, revealing a new lead vehicle that, in some cases, is slower than the original lead (the cutting-out) vehicle. We extracted the cut-out scenarios from an established real-world traffic dataset recorded by instrumented vehicles on Japanese highways and then defined using vehicle kinematic parameters (velocities and distances). The extracted scenarios were analyzed based on the direct correlation between every two consecutive vehicles: a rear part that describes the correlation between the following vehicle and the cutting-out vehicles; and a frontal part that describes the correlation between the cutting-out vehicle and the preceding vehicle. Parameter ranges were quantified with a regression model and determined based on the risk acceptance threshold applied in the field of Japanese high-speed trains and annual exposure by professional highway drivers to produce a scenario space with a reasonably foreseeable range in which ADS may not produce crashes lest it performs worse than human drivers. A multi-dimensional distribution analytical approach was used to derive a correlation between the following vehicle and the preceding vehicle considering the initial longitudinal velocities. Results suggest that there should be no risk of a collision between the following vehicle and the preceding vehicle when the time headway between the following vehicle and the cutting-out vehicle is equal to or more than 2 s. These findings can help to understand normative driver behavior during cut-out scenarios and to generate accident-free scenario space for which ADS must perform flawlessly.

Predictive Cruise Controller for Electric Vehicle to Save Energy and Extend Battery Lifetime

Electric vehicles are considered the most effective solution to the petroleum crisis and reduction of air pollution. In order to enhance energy efficiency and battery lifetime, this paper designs a predictive cruise controller (EC) for electric vehicles. Road information and traffic preview are employed for velocity planning while minimizing energy usage, maintaining battery health, and avoiding collision with a lead vehicle. To enable real-time implementation, we apply a model predictive control (MPC) framework formulated in space domain, and approximation and relaxation are introduced to obtain a smooth nonlinear program. Simulation results indicate that the proposed controller yields suboptimal performance as compared to the globally optimal solution. For higher practicability on real-life scenarios, we develop an enhanced EC that is capable of optimizing the stopping of the ego vehicle. According to the car-following studies where the lead vehicle is driven using real-life data, the enhanced EC achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$7.14 \%$</tex-math></inline-formula> energy saving and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$29.81 \%$</tex-math></inline-formula> battery life extension when compared to the intelligent driving model. The computation time of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$40 \,\mathrm{ms}$</tex-math></inline-formula> per MPC update also demonstrates its potential for real-time applications.

Hierarchical Planar Platoon Control Toward Tracking Safety for Distributed-Driven Vehicles

This paper deals with the planar platoon control problem of distributed-driven electric vehicles considering modeling deviations and driving disturbances. The follower vehicle is arranged to track the leader vehicle on curved roads with high tracking accuracy and driving security. A hierarchical integrated control framework is proposed based on disturbance-rejection robust model predictive control (DRRMPC) strategy and optimal control allocation (OCA) approach. The upper layer is a feedforward control layer, where a linear time-varying (LTV) model predictive controller (MPC) produces constrained predictive control towards a nominal model. The time-interval-based reference states are obtained from a “shadow following” trajectory generator through vehicle-to-vehicle (V2V) communication. In the middle layer, the feedback control consisting of a robust controller (RC) and the disturbance compensation out of an extended state observer (ESO), take effect to reconfigure the LTV vehicle model into the nominal model with an off-line calculated constraint tube. The bottom layer performs optimal control allocation among decoupled motor torques and wheel angles towards minimal tire force utilization. Hardware-in-the-Loop (HIL) experiment validates the effectiveness and practicability of the proposed platoon control strategy.

A Safe Decision Making Framework for Automated Vehicle Navigation among Human Drivers

In the near future, automated vehicles (AVs) will have to interact closely with Human-driven vehicles (HDVs). This work proposes an integrated decision-making framework that considers HDVs motion, a feasibility check, and planning. A learning-based encoder-decoder Long Short-Term Memory is used for HDV motion prediction. An error ellipse is used to capture the uncertainty from the learning-based model. A feasibility check is carried out to confirm the existence of a lane change trajectory from the given target vehicle's future position. The results from the feasibility check decide the action of AV. This work uses a lower-order parametric curve for path planning combined with an efficient trapezoidal acceleration-based velocity planner. Simulation results show that the proposed method guarantees a collision-free path for a lane changing scenario, given the lead vehicle position.

Too close for comfort: A mixed methods study to understand self-reported tailgating using the theory of planned behaviour

BackgroundTailgating (following a lead vehicle too closely) is a key contributor to crashes and injury. While vehicle technology has the potential to reduce the trauma resulting from tailgating, full market penetration of these technologies is some time away. In the meantime, efforts to improve road safety can focus on supporting safer driver behaviour by targeting motivations for this behaviour. MethodA mixed methods design was used to understand reasons why drivers tailgate and potential countermeasures to reduce this behaviour. Qualitative data from 247 drivers (males = 29 %; mean age = 39.86; SD = 14.39) were sought to understand circumstances when drivers are tailgated and when they report tailgating. In a second study, 736 drivers (males = 41 %; mean age = 37.69; SD = 14.27) responded to questions developed from the qualitative findings to quantify the frequency of tailgating and reasons behind it. The theory of planned behaviour (TPB) was applied to understand whether self-reported intention to tailgate could be predicted by attitudes, perceived social acceptance of the behaviour, perceived behavioural control, and past tailgating behaviour. ResultsTailgating was a common behaviour. All drivers in the first study had experienced being tailgated by other drivers, while 77% had tailgated other vehicles; albeit 55% reported this was rare. Tailgating was unintentional (due to dense traffic; or lack of knowledge of safe following distance recommendations) or intentional (due to pressure from other drivers, anger or to change others’ behaviour). Structural equation modelling showed that TPB constructs of attitude, social norms, perceived behavioural control and past tailgating behaviour predicted intention to tailgate, accounting for 66% of the variance. ConclusionTPB is a useful framework for explaining tailgating behaviour, or at least the intention to tailgate, and to develop interventions. These could focus on education of the risks of tailgating, the recommended safe following distances as well as strategies to support drivers maintaining safe following distances across different speed zones.

Spacing-speed dependency on relative speeds to the adjacent lanes: a statistical test

The spacing of a vehicle is the spatial separation between this vehicle and the vehicle ahead. It forms the basis of the fundamental diagram of traffic flow. Traditional car-following models assume that the spacing of a following vehicle is strictly a function of the speed of the lead vehicle in the same lane. The recent literature, however, reports a phenomenon of spacing-speed dependency on the relative speeds to the adjacent lanes, termed speed differential effect. The aim of this paper is to statistically test the speed differential effect. To quantitatively measure the speed differential effect, we develop a mixture spacing-speed model to capture the probabilistic nature of the spacing and speed relationship, and then relate this spacing-speed relation to the relative speeds to the adjacent lanes. We apply the developed model to test the speed differential effect. Our empirical analysis provides strong statistical evidence to support the speed differential effect.

Impacts of cooperative adaptive cruise control and cooperative lane changing on delay and riding comfort in autonomous car–autonomous truck mixed traffic

This study examines the impacts of cooperative adaptive cruise control (CACC) and cooperative lane changing (CLC) on the delay and the riding comfort in autonomous car–autonomous truck (AC–AT) mixed traffic at a freeway merging area. For this task, AC–AT mixed traffic on a 5.25 km section of freeway was analyzed using the Aimsun Next microscopic traffic simulation. The effects of different CACC parameters and CLC on the average speed and acceleration distributions as the measures of delay and riding comfort, respectively, were evaluated. It was found that (1) lower sensitivity to the lead vehicle reduced the merging time, (2) shorter time gaps between autonomous vehicles and between platoons decreased the delay, and (3) longer time gaps reduced the delay at higher percentage of ATs. These results demonstrate that the delay of AC–AT mixed traffic at a freeway merging area can be reduced and riding comfort can be increased by adjusting CACC parameters.

Impact of Temporary Browsing Restrictions on Drivers’ Situation Awareness When Interacting with In-Vehicle Infotainment Systems

Looking away from the road during a task degrades situation awareness of potential hazards. Long glances back to the road rebuild this awareness and are thought to be critical for maintaining good vehicle control and recognizing conflicts. To further investigate the importance of rebuilding situation awareness, a controlled test-track study was performed that evaluated drivers’ hazard awareness and response performance to a surprise event after completing a task that involved pausing partway through it to look back at the road. Thirty-two drivers completed a visual-manual infotainment system secondary task. Half of the drivers were instructed to pause their browsing mid-task, while the others were not. While the task was being performed, a lead vehicle activated its hazard lights. It then unexpectedly dropped a fake muffler once drivers completed the task. Drivers’ visual attention to the road and their ability to respond to the muffler were measured. The drivers that paused their browsing were more aware of the lead vehicle’s hazard lights, showed less surprise to the dropped muffler, and executed more measured avoidance maneuvers compared to the drivers that did not pause their browsing. These findings suggest that drivers’ situation awareness can be better maintained when task interactions are paced, allowing for longer monitoring of the environment. Mechanisms that encourage drivers to take restorative on-road glances during extended browsing may be a key aspect of an overall approach to mitigating driver distraction.

Effects of simulated time delay on teleoperators’ performance in inter-urban conditions

Time delay in communication networks is a major challenge in controlling vehicles from remote centers (also known as teleoperation). In the current study, we investigated the effects of time delay in controlled and partly controlled driving environments at higher driving speeds and in relatively short time delays (50 ms, 150 ms, and 250 ms) which characterise the 5G era. Participants (N = 72) faced several challenges while operating a vehicle in a simulated scenario. The challenges started with following a lead vehicle on a curve, continued with responding to a sudden brake of the lead vehicle, and concluded with highway driving among other simulated road users. The speed, the distance to other vehicles, the brake and swerving of the vehicle and the crash rate were measured during each scenario. At the end of the scenario, participants completed the NASA-TLX. We found that at a time delay of 250 ms (but not 150 ms), following a lead vehicle on a curved road was characterized by a higher standard deviation of speed and distance to the lead vehicle and stronger swerving than at the shorter time delay of 50 ms. Swerving was also stronger at the 250 ms than at the 50 ms delay on the highway. Finally, crashes, which occurred mainly on the highway and in response to the sudden brake of the lead vehicle (before the highway), were 20 % (not statistically significant) higher at the 250 ms than at the 50 ms delay. The gap in collision frequency between the 150 ms and 50 ms delay was lower (8 %) and not statistically significant. This relatively small gap corresponded with the insignificant changes between 150 ms and 50 ms delay on the other indices of driving safety that we analyzed (e.g., braking and swerving). Regarding the NASA-TLX, participants rated the 250 ms delay higher than the other delays on almost all dimensions except for effort on which they did not distinguish between the 250 ms and the 150 ms delay. Our findings expand current knowledge on the effects of time delay on teleoperation. In the Discussion section, we translate this knowledge into practical recommendations regarding possible delays in teleoperation and possible countermeasures for the time delay.

Optimal Powertrain Energy Management for Autonomous Hybrid Electric Vehicles With Flexible Driveline Power Demand Using Approximate Dynamic Programming

The increasing number of vehicles, the excessive use of fossil fuels, and the related safety and environmental issues have motivated studies on autonomous vehicles and Hybrid Electric Vehicles (HEVs). In this article, we focus on the control of the powertrain energy management for an autonomous HEV. A new powertrain control strategy is enabled by leveraging one of the uniqueness in the powertrain management of an autonomous vehicle, i.e., the instantaneous power generated by the powertrain does not need to exactly follow the power demand by the vehicle motion controller. This is referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flexible power demand</i> , which adds an extra degree of freedom to the powertrain energy management, and can lead to control design achieving better fuel economy. The powertrain control is then formulated under the Approximate Dynamic Programming (ADP) framework, and the power flexibility is incorporated in the ADP formulation. At last, an example of multiple connected HEVs following a leader vehicle operating in an off-road scenario is given to demonstrate the feasibility of the proposed method.

A Distributed Platoon Control Framework for Connected Automated Vehicles in an Urban Traffic Network

In this article, we consider a specific scenario, where multiple connected automated vehicles (CAVs) entering one road link from different upstream links are required to form a platoon first, then cross the junction at the end of the link safely, and fuel economically. Each CAV may experience a reaction-time delay and follow a specific nonlinear car-following model. To address this problem, we design a two-stage distributed platoon control strategy. In the first stage, we present a sufficient condition about the length of a platoon that ensures a subsequent safe junction crossing, upon which a distributed cooperative control protocol is designed and an iteration algorithm is proposed to ensure the fast formation of the platoon in a finite time. Once the platoon is formed, in the second stage, we design a method to estimate the leader vehicle’s acceleration adjustment by developing distributed observers that ensure a safe junction crossing of the entire CAV platoon, taking into account follower-vehicle dynamics and an upcoming traffic signal schedule while minimizing the overall platoon fuel consumption. If a platoon has already been formed at the entrance of the link with the appropriate platoon size, then only the second-stage design is needed. Thus, our proposed two-stage CAV link control strategy may potentially become part of a broader fuel-economic solution for the effective management of CAV platoons in a large urban traffic network.

Modelling the effect of aggressive driver behavior on longitudinal performance measures during car-following

Driving aggression is a major concern during car-following situations as it is closely associated with collision risk and crash severity. Despite the tendency of reckless driving actions and increased crash risk with the vehicle ahead, aggressive driver behavior remains unexplored. The present study aimed to investigate the effects of aggressive driver behavior (three driver categories: aggressive, moderately aggressive, and non-aggressive drivers) and drivers’ tendency to aggressive stimuli (due to lead vehicle (vehicle moving ahead) reckless driving) on car-following behavior. A sample of fifty-eight Indian drivers participated in this study. All the experiments were conducted using a driving simulator. The simulator design includes a lead vehicle dynamic maneuver with rapid accelerations and decelerations (at 1 m/s2, 1.5 m/s2, and 2 m/s2). Three longitudinal performance measures including speed variability (SPV) from LV, speed recovery time (SRCT), and time spent tailgating, were analyzed during LV acceleration stage using generalized linear models (GLM). Further, to assess collision avoidance behavior and crash risk, deceleration adjusting time (DAT) was analyzed during the LV sudden deceleration stage using Weibull Accelerated Failure Time (AFT) analysis. The findings indicate that due to their tendency to risk-raking behavior, aggressive drivers decreased their SPV and SRCT by 25 % and 18 %, respectively. The time spent tailgating increased by 107 % for aggressive drivers. The survival probabilities were decreased by 23.77 % for aggressive drivers at 3 sec DAT. The findings of the study can be used in car-following models to enhance the model performance, improve the reliability of simulation tools, and design interventions to improve safety (in automated driving).

Interaction between two car models with application to pollutant dispersion

Nowadays, air quality monitoring is a major concern. Among pollutants, exhaust UltraFine Particles (UFP) have a harmful effect on health due to their small size and to their ability to penetrate the bloodstream, the respiratory system, the brain as well as the placental barrier. Out of the industrial emissions, road traffic is a major source of air pollution. UFP emitted from the cars can infiltrate the car cabin through leakages and ventilation system leading to poor air quality in the passenger compartment. Improving the knowledge regarding the dispersion of exhaust UFP in the wake of a car considering road traffic is then mandatory. In this paper, the dispersion of UFP is studied in the wake of a leading vehicle (LV) followed by a second one. Two rear slant angles (φ) for LV and 6 inter-vehicle distances are considered. Velocities and UFP concentrations are collected in a wind tunnel. The influence of the rear slant angle of the leading vehicle is discussed on both wake dynamics and concentration fields. The results indicate that a critical inter-vehicle distance can be identified above which the recirculation region remains unchanged. Considering the concentration fields, for a leading vehicle with φ=0°, the large recirculation region tends to entrap the particles and to homogenize the particle mixing. For φ=25°, the attachment of the flow in the rear slant angle has a strong impact on the particle dispersion. It leads to a small recirculation region, which entraps particles leading to a non-homogeneous mixing. The Particle Concentration fields in Number (PNC) reveal high levels in the exhaust tailpipe plane that can contribute to lower PNC levels in the car cabin. Comparisons with previous studies are discussed and possible recommendations are suggested.

Effect of Situation Kinematics on Drivers’ Rear-End Collision Avoidance Behaviour—A Combined Effect of Visual Looming, Speed, and Distance Analysis

Considering the large proportion of rear-end collisions occurring in our daily life and the severity it may lead to, the objective of this study was to investigate the effect of situation kinematics on drivers’ rear-end collision avoidance behaviour after brake onset. A wide range of lead vehicle deceleration scenarios were designed based on driving simulator experiments to collect drivers’ deceleration behaviour data. Different from measures (e.g., speed, the lead vehicle’s deceleration et al.) often adopted in previous studies, a visual looming-based measure at different time points was calculated combined with analysis of speed and distance to quantify situation kinematics in this study. The Spearman’s nonparametric rank correlation test was firstly conducted to examine the correlation between visual looming-based metrics and related deceleration behaviour. The mixed model was performed on drivers’ brake jerk and maximum deceleration rate, while the logistic model was then performed to predict the probability of the occurrence of rear-end collisions. Spearman’s nonparametric test showed that both deceleration ramp-up and drivers’ maximum deceleration rate increase significantly as the looming traces increase faster. Results of the logistic model indicated that the probability of occurrence of a potential collision might be higher if the situation at the brake onset is quite urgent and braking is moderate. It was demonstrated that both drivers’ deceleration ramp-up and maximum deceleration rate could be highly kinematic-dependent, and visual looming, driving speed, and distance can be useful information for drivers to take relative deceleration actions.

Operational Impact of the Through-Traffic Signal Prioritization for Heavy Commercial Vehicle Platooning on Urban Arterials

This study investigated the operational impact of heavy commercial vehicle (HCV) platooning on urban arterials. HCV platooning is an important application of vehicle-to-vehicle (V2V) technology, with urban arterials facilitating an essential component of HCV movements when picking up and delivering goods. HCV platooning has the potential to reduce fuel consumption and emissions. Moreover, the increasing HCV driver shortage problem can be alleviated if the vehicle following behind a lead vehicle can function without a driver by using autonomous technology enabling Society of Automotive Engineers Level 4 or higher. PTV VISSIM was used to develop a set of micro-simulation models that investigated the impact of traffic signal priority (TSP) and low levels (0%, 5%, and 10%) of HCV platooning. The performance measures include travel time and the number of stops. With the existing traffic control system, HCV platooning increased travel time and increased the number of stops for all vehicles including passenger cars and HCVs. TSP with 5% HCV platooning improved travel time and decreased the number of stops for all vehicles. TSP with 10% HCV platooning, however, only decreased travel time and the number of stops for passenger vehicles. The results suggest that a higher penetration rate of HCV platooning may create significant delays and overwhelm the traffic system even with the assistance of TSP. The findings of this study highlight the potential for TSP to mitigate the impact of HCV platooning on traffic congestion. However, the TSP system may not be a panacea that works for all traffic compositions.

Data-Driven Leading Vehicle Speed Forecast and Its Application to Ecological Predictive Cruise Control

In this article, we propose an ecological predictive cruise control method for connected and automated vehicles (CAVs) using data-driven predicted leading vehicle speed in a car-following scenario. Many existing studies assume that the leading vehicle's behavior is known when planning the trajectory of the ego vehicle. Unfortunately, predicting the future behavior of adjacent vehicles is extremely uncertain and inaccurate for use in control. To overcome this, we adopt the vector autoregressive model (VAR) that is well suited for generating simultaneous forecasts of the response variables when predicting the short-term behavior of the vehicle ahead. As many human drivers behave similarly in a car-following situation, vehicle connectivity is specifically used to drop hints of the behaviors of the cars following the connected car. Once the leading vehicle's future trajectory is predicted, the ego vehicle is controlled in a way that minimizes energy consumption by optimizing its speed trajectory while remaining safe. Through simulation case studies, we demonstrate that our approach can achieve improved energy efficiency considerably than the conventional strategy that cannot predict the speed of vehicles ahead. Given the recent market penetration of vehicle connectivity technologies and advanced driving assistance systems (ADAS), the proposed method is expected to have a high commercialization potential.

Dynamic Event-Triggered Platooning Control of Automated Vehicles Under Random Communication Topologies and Various Spacing Policies.

This article addresses the problem of dynamic event-triggered platooning control of automated vehicles over a vehicular ad-hoc network (VANET) subject to random vehicle-to-vehicle communication topologies. First, a novel dynamic event-triggered mechanism is developed to determine whether or not the sampled data packets of each vehicle should be released into the VANET for intervehicle cooperation. More specifically, the threshold parameter in the triggering condition is dynamically adjusted over time according to the vehicular data variations, the dynamic threshold updating laws, and the bandwidth occupancy indication. Second, a unified platooning control framework is established to account for various spacing policies, randomly switching communication topologies, unknown leader control input, and external disturbances. Then, a new scheduling and platooning control co-design approach is presented such that the controlled vehicular platoon can successfully track the leader vehicle under random communication topologies and different spacing policies, including constant spacing, constant time headway spacing, and variable time headway spacing, meanwhile maintaining efficient bandwidth-aware resource management. Finally, comparative studies are provided to substantiate the effectiveness and merits of the proposed co-design approach.