Car-following Situation Research Articles

Evaluation of Conventional Surrogate Indicators of Safety for Connected and Automated Vehicles in Car Following at Signalized Intersections

The driving behaviors of connected and automated vehicles (CAVs) will differ from those of human-driven vehicles (HDVs) because the CAVs’ driving decisions are controlled by computers. Because of the limited amount of crash data for CAVs, researchers have relied on surrogate measures of safety to assess their safety impacts. However, they often use the same safety indicators for CAVs that were used for HDVs, raising questions about the adequacy of the safety indicators for CAVs. This study aims to investigate the suitability of using conventional safety indicators for CAVs. To achieve this, we evaluated eight safety indicators used for CAVs in the literature: time-to-collision (TTC), post-encroachment time (PET), time-exposed TTC, time-integrated TTC, deceleration rate to avoid a crash (DRAC), crash-potential index, rear-end-collision risk index, and potential index for collision with urgent deceleration (PICUD). For the evaluation, we first simulate CAVs on an approaching lane of signalized intersections using the acceleration-control algorithm. The algorithm replaces the HDV trajectories with CAVs for mixed simulations where HDVs and CAVs coexist. Analyzing the simulation output, we examined the safety indicators for the various car-following scenarios and the CAV proportions. The findings suggest that PET and PICUD can yield different safety implications for CAVs because of their small-gap car-following characteristics. Ignoring such characteristics may lead to interpreting the small-gap car-following situations as simply dangerous traffic interactions for CAVs. The car-following experiments indicate that TTC, PET, and DRAC are insufficient in measuring the safety implications when successive vehicles operate at similar speeds for CAVs.

Inter- and Intra-Driver Reaction Time Heterogeneity in Car-Following Situations

Inter- and Intra-Driver Reaction Time Heterogeneity in Car-Following Situations

How various urgencies and visibilities influence drivers’ takeover performance in critical car-following conditions? A driving simulation study

Drivers of Level 3 automated vehicles are relieved from the driving task in specific circumstances but are required to take over control once the takeover request is prompted. Previous studies have investigated drivers’ takeover performance in non-critical car-following. However, little is known about drivers’ takeover behaviors in critical car-following, especially in low-visibility weather, which remarkably increases the risk of car-following. A driving simulator experiment with a 2 × 3 × 3 factor within-group design was conducted. The design matrix contained two weather conditions (clear and foggy), three car-following time headways (2 s, 3 s, 4 s) and three deceleration rates of the lead vehicle (LV) (0, 2 m/s2, 4 m/s2). A total of 30 participants completed the experiment. The results showed that in critical car-following situations, drivers faced greater challenges in negotiating with adjacent vehicles rather than the LV itself. Urgency and visibility did not significantly impact the likelihood of a crash with the LV due to drivers’ adoption of stronger braking. However, decreased visibility and higher LV’s deceleration increased the crash rate when drivers attempted lane changes. As urgency increased, drivers tended to change lanes earlier, leading to higher lane-changing risks and compromised lateral stability. This study can provide some insights for the car-following strategies of automated driving vehicles and the design of dedicated takeover schemes in various transportation environments.

Safety evaluation of mixed traffic flow with truck platoons equipped with (cooperative) adaptive cruise control, stochastic human-driven cars and trucks on port freeways

This study examines the operational safety levels of a novel, complex traffic flow mixed with truck platoons equipped with (cooperative) adaptive cruise control, known as TPs-(C)ACC (referred to as TPs), as well as traditional human-driven cars (HDCs) and trucks (HDTs) in various scenarios on port freeways. The stochastic behavior of human drivers in car-following situations is captured using the stochastic intelligent driver model (SIDM). In contrast, the car-following behaviors of TPs are modeled using the Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) models, respectively. Surrogate safety measures (SSMs) are employed to evaluate the safety performance of the mixed traffic flow. The experimental findings demonstrate that, all else being equal, the oscillation of the mixed traffic flow considered in this study diminishes as the penetration rate and lengths of TPs increase, respectively. The safety levels of the mixed traffic flow would be improved with longer TPs but deteriorate with higher total traffic flow rates. For a given CACC intra-platoon headway, larger headways of ACC truck leaders are advantageous to enhancing the safety levels of the mixed traffic flow. However, for TPs with a shorter headway of leading trucks, higher penetrations of TPs with shorter platoon lengths would worsen the safety levels of the mixed traffic flow. When considering a fixed combination of penetration rates, replacing an ACC truck leader with a CACC truck leader improves the safety of the mixed flow, while the incremental effects of lengthening the TPs on enhancing the safety levels diminish. A mixed flow with longer TPs is more susceptible to the stochastic behavior of human drivers.

A Multi-Agent Driving-Simulation Approach for Characterizing Hazardous Vehicle Interactions between Autonomous Vehicles and Manual Vehicles

The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs and MVs coexist in the current road infrastructure will continue for a considerably long period of time. The purpose of this study is to develop a methodology to evaluate the driving safety of mixed car-following situations between AVs and MVs on freeways based on a multi-agent driving-simulation (MADS) technique. Evaluation results were used to answer the question ‘What road condition would make the mixed car-following situations hazardous?’ Three safety indicators, including the acceleration noise, the standard deviation of the lane position, and the headway, were used to characterize the maneuvering behavior of the mixed car-following pairs in terms of driving safety. It was found that the inter-vehicle safety of mixed pairs was poor when they drove on a road section with a horizontal curve length of 1000 m and downhill slope of 1% or 3%. A set of road sections were identified, using the proposed evaluation method, as hazardous conditions for mixed car-following pairs consisting of AVs and MVs. The outcome of this study will be useful for supporting the establishment of safer road environments and developing novel V2X-based trafficsafetyinformation content that enables the enhancement of mixed-traffic safety.

Driving Safety Evaluation of Mixed Car-Following Situations by Autonomous and Manual Vehicles at Urban Interrupted Road Facilities

In the early stages of the development of autonomous driving technology, autonomous vehicles (AVs) and manually driven vehicles (MVs) will both be present on the roads, and the interaction of AVs and MVs will affect driving safety. This study aims to evaluate driving safety for car-following scenarios involving AVs and MVs on urban roads and to identify the driving safety vulnerability sections for each road. In this study, the AV behavior control algorithm and the urban interrupted road were implemented using SCANeRTM, and longitudinal, lateral, and inter-vehicle driving safety indicators were derived. As a result of the analysis, the driving safety of the AV-AV pair was the highest in all safety indicators, and the mixed pair (AV-MV) was safer than the MV-MV pair. The driving safety evaluation index for each analysis section was analyzed by the rate of change. As a result of analysis of variance, the null hypothesis that the section was the same in all mixed pairs of evaluation indicators was rejected. Post hoc analysis shows that the section with the greatest difference from the straight line was selected as a vulnerable area. As a result of the post hoc analysis, the non-signal intersection was analyzed as the most vulnerable area in the case of the mixed pair. Using this, it is possible to select a driving safety vulnerability section when AVs and MVs are mixed on actual urban roads.

Revealing the impact of stochastic driving characteristics on car-following behavior with locally collected vehicle trajectory data

This study investigates the impact of Chinese drivers’ stochastic behaviour on local car-following situations using localized trajectory data. An extended stochastic car-following model (S-IDM) is established, which considers both internal and external stochasticity. External stochasticity is characterized by different driver types, while internal stochasticity is characterized by the standard deviation of acceleration under different headway and velocity differences for the same driver. The proposed model shows advantages in terms of single-vehicle simulation accuracy and traffic shock reproduction ability, compared to traditional and existing car-following models. The model can also be extended to the evolution analysis of mixed traffic flow models, where reducing the stochasticity of human-driven vehicles is critical for optimizing and controlling traffic flow.

Applying the Accumulator model to predict driver’s reaction time based on looming in approaching and braking conditions

Introduction: The prediction of when the driver will react to a change in the lead vehicle motion is critical for assessing rear-end crash risk using car-following models. Past studies have assumed constant reaction time and driver’s continuous reaction. However, these assumptions are not valid as the driver’s reaction time can vary in different car-following situations and the driver does not continuously react to the lead vehicle motion. Thus, this study predicted the driver’s reaction time using the Wiedemann car-following model and the Accumulator model. The Accumulator model assumes the driver’s start of reaction based on the accumulation of looming and thereby reflects the driver’s intermittent reaction. Method: Fifty drivers’ behavior was observed using a driving simulator in two scenarios: (1) approach and follow a moving lead vehicle and (2) approach a stopped lead vehicle. The Accumulator model predicted the reaction times based on different looming variables (angular velocity and tau-inverse), lead vehicle type (car and truck), and lead vehicle brake lights (on or off). Results: The Accumulator model showed lower prediction errors of the reaction time than the Wiedemann model, which assumes reaction based on the fixed looming threshold. The Accumulator model predicted the reaction times more accurately when it was calibrated with the angular velocity due to width and height of lead vehicles. Moreover, the Accumulator model with tau-inverse produced the smallest prediction error of reaction times among different Accumulator models and the Wiedemann model when lead vehicle brake lights were on. Conclusions: This study demonstrates that the Accumulator model is a promising method of predicting the driver’s reaction time in car-following situations, which affects rear-end crash risk. Practical Applications: The Accumulator model can be incorporated into a car-following model for the prediction of reaction times and can estimate the rear-end collision risk of vehicles more accurately.

Evaluation of the driving performance and user acceptance of a predictive eco-driving assistance system for electric vehicles

In this work, a predictive eco-driving assistance system (pEDAS) with the goal to assist drivers in improving their driving style and thereby reducing the energy consumption in battery electric vehicles (BEVs) while enhancing the driving safety and comfort is introduced and evaluated. pEDAS in this work is equipped with two model predictive controllers (MPCs), namely reference-tracking MPC and car-following MPC, that use the information from onboard sensors, signal phase and timing (SPaT) messages from traffic light infrastructure, and geographical information of the driving route to compute an energy-optimal driving speed. An optimal speed suggestion and informative advice are indicated to the driver using a visual feedback. Moreover, the warning alerts during unsafe car-following situations are provided through an auditory feedback. pEDAS provides continuous feedback and encourages the drivers to perform energy-efficient car-following while tracking a preceding vehicle, travel at safe speeds at turns and curved roads, drive at energy-optimal speed determined using dynamic programming in freeway scenarios, and travel with a green-wave optimal speed to cross the signalized intersections at a green phase whenever possible. Furthermore, to evaluate the efficacy of the proposed eco-driving assistance system, user studies were conducted with 41 participants on a dynamic driving simulator. The objective analysis revealed that the drivers achieved mean energy savings up to 10%, reduced the speed limit violations, and avoided unnecessary stops at signalized intersections by using pEDAS. Finally, the user acceptance of the proposed pEDAS was evaluated using the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The results showed an overall positive attitude of users and that the perceived usefulness and perceived behavioral control were found to be the significant factors in influencing the behavioral intention to use pEDAS.

Exploring Microscopic Characteristics of Bicycle Riders’ following Behaviors in a Single-File Movement

Cycling can bring a wide range of social, economic, and health benefits to individuals and communities. The safety and efficiency of bicycle facilities can be significantly impacted by the interactions among riders. This study aims to examine the microscopic characteristics of how cyclists interact with each other when they are in a single file movement based on the trajectory data collected from an experiment. Reaction delay was obtained by optimizing the correlation between relative speed and acceleration curves for individual cyclists and it was found that even for a given cyclist, this characteristic time delay could vary considerably, and be situation-dependent. Furthermore, it was found that the distribution of reaction delay, which has an average (±SD) of 0.66 s (±0.33 s), followed a log-normal distribution. The strong correlation observed between relative speed and time-delayed acceleration resembles the behavior observed in car-following situations, highlighting that relative speed is an essential factor influencing the acceleration behavior of cyclists. Multiple linear regression models were used to understand the association between acceleration and other key microscopic variables, e.g., spacing and relative speed, which are commonly used in microscopic behavior models. While the spacing between cyclists was found to have a significant impact on acceleration behavior, its effect was not as significant as that of relative speed. The outcomes of this study provide valuable insights into the cyclists’ behavior and can aid in the development of microscopic simulation models.

Modelling braking behaviour of distracted young drivers in car-following interactions: A grouped random parameters duration model with heterogeneity-in-means

Braking is an important characteristic of driving behaviour that has a direct relationship with rear-end collisions in a car-following task. Braking becomes more crucial when drivers’ cognitive workload increases because of using mobile phones whilst driving. This study, therefore, investigates and compares the effects of using mobile phones whilst driving on braking behaviour. Thirty-two young licenced drivers, evenly split by gender, faced a safety–critical event, that is, leader’s hard braking, in a car-following situation. Each participant drove the CARRS-Q Advanced Driving Simulator and was required to respond to a braking event in the simulated environment in three phone conditions: baseline (no phone conversation), handheld, and hands-free. A random parameters duration modelling approach is employed to (i) model drivers’ braking (or deceleration) times using a parametric survival model, (ii) capture unobserved heterogeneity associated with braking times, and (iii) account for repeated experiment design. The model identifies the handheld phone condition as a random parameter whilst vehicle dynamics variables, hands-free phone condition, and driver-specific variables are found as fixed parameters. The model suggests that most distracted drivers (in the handheld condition) reduce their initial speeds more slowly than undistracted drivers, reflecting their delayed initial braking that may lead to abrupt braking to avoid a rear-end collision. Further, another group of distracted drivers exhibits faster braking (in the handheld condition), recognising the risk associated with mobile phone usage and delayed initial braking. Provisional licence holders are found to be slower in reducing their initial speeds than open licence holders, indicating their risk-taking behaviour because of their less experience and more sensitivity to mobile phone distraction. Overall, mobile phone distraction appears to impair the braking behaviour of young drivers, which poses significant safety concerns for traffic streams.

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).

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.

Hardware Development and Analysis of Vehicle's Driver Awareness during Braking Event

Safety is the most important factor in both manufacturing vehicles as well as when driving and riding. Passive and active system are the two main categories of safety system, thus, this project focus on the active system which where the brake system is located to avoid any collision in critical situation especially rear-end collision. The main objective of this project is to study the driver’s braking response in car-following situations. Data Acquisition System using Arduino IDE was developed, which lead to analyzing the driver’s brake response during emergency braking event. Two types of vehicles were used, which are the Yamaha 135LC motorcycle and Perodua Myvi 1.3L, which also known as Test Vehicle 1 (TV 1) and Test Vehicle 2 (TV 2) respectively. The Data Acquisition System (DAQ) system to record the brake response time of driver in TV 2 as the driver reacts to the activation of brake lights of TV 1 by using the ESP8266 (Wi-Fi controller) was developed. The DAQ installed in TV 1 was connected wirelessly to the DAQ installed in TV 2. Arduino IDE software was used as a medium to transfer data from the push-button sensor to the laptop with the assist of ESP8266 board as the microcontroller, that is connected to the push-button sensor. Two brake lights were tested, which were standard brake lights and flashing brake lights. Each brake lights were tested with three different speeds, which are 20, 30 and 40 km/h. Experiment was carried out at night, where TV 1 braked at any straight road around the campus. Generally, the results indicates that for each speed tested for both brake lights, the brake response time increases as speed increases. The overall results suggested that flashing brake lights has a significantly shorter brake response time with average of 145, 144, 173 ms for 20, 30 and 40 km/h respectively. With average percentage of 12.8% faster brake response time.

A Car-Following Model Based on Trajectory Data for Connected and Automated Vehicles to Predict Trajectory of Human-Driven Vehicles

Connected and Automated Vehicles (CAV) have been rapidly developed, which, inevitably, renders that human-driven and autonomous vehicles share the road. Thus, trajectory prediction is an important research topic, which helps each CAV to efficiently follow a Human-Driven Vehicle (HV). In a wider scope, trajectory prediction, also, helps to improve the throughput of traffic flow and enhance its stability. To realize the trajectory prediction of Connected and Automated Vehicles to Human-Driven Vehicles, a car-following model, which is based on trajectory data, was established. Adding deep neural networks and an Attention mechanism, this paper established a data-driven car-following model, based on CNN-BiLSTM-Attention for CAV, to predict trajectory, by referring to the modeling idea of the traditional car-following model. The trajectory data in the next-generation-simulation (NGSIM) datasets that match the car-following characteristics were selected. In addition, noise-reduction pre-processing of the trajectory data was performed, to make it match the actual car-following situation. Experiments, for selecting the optimal structure of the model and the method of trajectory prediction, were carried out. The data-driven car-following models, such as LSTM, GRU, and CNN-BiLSTM, were selected for comparative analysis of trajectory prediction. The results show that the CNN-BiLSTM-Attention model has the smallest MAE and MSE as well as the largest R2. The CNN-BiLSTM-Attention model has the highest accuracy in vehicle-trajectory prediction. The model can, effectively, realize vehicle-trajectory prediction and provide a theoretical basis for vehicle-trajectory-based velocity guidance of Human-Driven Vehicles. In the future, the model can, also, provide the theoretical basis for Connected and Automated Vehicles, to make car-following decisions in mixed traffic flow.

Personalized Speed Planning Algorithm Using a Statistical Driver Model in Car-following Situations

Personalized Speed Planning Algorithm Using a Statistical Driver Model in Car-following Situations

Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers' Driving Styles.

This study investigated drivers' subjective feelings and decision making in mixed traffic by quantifying driver's driving style and type of interaction. Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers' subjective feelings and decision making were collected via questionnaires. Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Driving style and type of interaction significantly influenced drivers' subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience.

Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation (SAE Level 3) or monitored the drive (SAE Level 2). Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ∼ 18 min each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. Results showed that driver workload due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that a lead vehicle maintain a shorter THW can significantly increase driver workload during takeover scenarios, potentially affecting driver safety. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional cognitive or attentional demands on the driver. Our results indicated that ECG and EDA signals are sensitive to variations in workload, which warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help future driver monitoring systems respond appropriately to the limitations of the driver, and predict their performance in the driving task, if and when they have to resume manual control of the vehicle after a period of automated driving.

Car-Following Properties of a Commercial Adaptive Cruise Control System: A Pilot Field Test

Automated driving systems, which can take over certain dynamic driving tasks from the driver, are becoming increasingly available in commercial vehicles. One of these automated driving systems widely introduced in commercial vehicles is adaptive cruise control (ACC). This system is designed to maintain certain desired driving speeds and time headways as chosen by drivers and based on the settings available within the system. The properties and actual performance of these systems will affect the traffic flow and its stability. However, the specific properties and their workings are rarely publicly available. Therefore, the main aim of this paper is to test the actual performance of a commercial ACC system under different desired speed and distance gap settings, as well as driving modes in a car-following situation. For this purpose, a pilot field test was conducted in the Netherlands in which two identical commercial vehicles equipped with ACC systems were driven simultaneously. The first vehicle was used to create a pre-specified speed profile by adapting the ACC system settings manually, whereas the second vehicle followed the lead vehicle when the ACC system was engaged to test its actual performance. The main findings indicate that the different system settings affect the car-following indicators, and system response times were found to be comparable to human response times. The eco mode was found to affect some of the car-following indicators, and it does not deteriorate safety below the safety level of driving with short headway setting in drive mode.

Effects of Time-Gap Settings of Adaptive Cruise Control (ACC) on Driver’s Risk Feeling Estimated by the Car-Following Situation

Effects of Time-Gap Settings of Adaptive Cruise Control (ACC) on Driver’s Risk Feeling Estimated by the Car-Following Situation