Driving Behavior Model Research Articles

How left-turning vehicles deal with conflicts at intersections: A driving behavior model based on relative motion risk quantification

How left-turning vehicles deal with conflicts at intersections: A driving behavior model based on relative motion risk quantification

Review of driver behaviour modelling for highway on‐ramp merging

AbstractAutonomous driving is an exciting research field that has received growing attention in recent years. One of the most challenging and safety‐critical driving situations is highway on‐ramp merging. Most decision‐making strategies that perform highway on‐ramp merging are designed, firstly, to reduce the risk of crashes and improve the safety metrics. However, even with the development of such advanced driving systems, human drivers will still be involved in road traffic. Human drivers have various driving styles and different reactions to other traffic participants on the highway on‐ramp. Understanding driver behaviors is essential for designing safe and efficient real‐world driving strategies. Therefore, this paper provides a unique systematic review of existing techniques for modelling driver behaviors at highway on‐ramps, which are critical locations for traffic safety and efficiency. The novelty of this review is that it proposes a new classification of current state‐of‐the art techniques. Each category of techniques involves a unique paradigm. For each category of approaches, fundamental concepts are examined together with their challenges and limitations, and an overview on practical implementation. Furthermore, and based on the classification and chronological order, current research trend is identified, i.e. “data‐driven approaches”. Some future research avenues and disparities are also discussed.

Drivers’ reactions to real-world forward collision warnings at both macroscopic and microscopic longitudinal levels: A functional approach

Understanding drivers’ reactions to in-vehicle forward collision warnings (FCWs) is vital for advancing FCW design and improving road safety. However, past studies often used aggregated safety measures to analyze the drivers’ reactions to FCWs, thereby at the microscopic level, limiting our ability to understand drivers’ reactions to FCWs at particular timestamps immediately after FCWs are issued. Additionally, there has been a notable absence of studies at the macroscopic perspective focusing on analyzing how drivers’ reactions to FCWs evolve over an extended period of time. To overcome these two limitations, this study proposes a new research framework using Functional data analysis (FDA) approach to model driver behavior profile in response to FCWs at both microscopic and macroscopic longitudinal levels. Real-world FCW data collected from the New York City Connected Vehicle Pilot Deployment project is used for the case study. At the microscopic level, a sparse functional design is adopted to model driver behavior profiles, accounting for irregularly spaced functional measurements. Nonparametric functional linear regression is then used to estimate the drivers’ reactions to FCWs at a particular timestamp immediately after FCWs are issued. At the macroscopic level, the functional two-sample test and a functional distance metric are used to examine changes in drivers’ reactions to FCWs over the study period and quantify the magnitude of these changes. Time to collision (TTC) and modified time to collision (MTTC) measures are used to represent driver behavior profiles, and both TTC and MTTC after FCWs are issued are modeled as functions with respect to time based on the proposed FDA approach. Compared to using aggregated safety measures including minimum TTC and MTTC as well as mean TTC and MTTC, new patterns of drivers’ reactions to FCWs are unveiled at both microscopic and macroscopic longitudinal levels. Study outputs reveal several key insights, including driver compensation behavior that escalates safety risk after an initial safety improvement and the diminishing safety benefits of FCWs from the beginning to the end of the after period. The proposed research framework can be generalized to analyze various types of in-vehicle driver warnings at both microscopic and macroscopic longitudinal levels. The findings of this study can support the calibration of detailed driver response behavior to in-vehicle warnings and facilitate the design of driver warning applications and further investigation of their safety benefits.

A Three-Stage Cellular Automata Model of Complex Large Roundabout Traffic Flow, with a Flow-Efficiency- and Safety-Enhancing Strategy.

Intelligent transportation systems (ITSs) present new opportunities for enhanced traffic management by leveraging advanced driving behavior sensors and real-time information exchange via vehicle-based and cloud-vehicle communication technologies. Specifically, onboard sensors can effectively detect whether human-driven vehicles are adhering to traffic management directives. However, the formulation and validation of effective strategies for vehicle implementation rely on accurate driving behavior models and reliable model-based testing; in this paper, we focus on large roundabouts as the research scenario. To address this, we proposed the Three-Stage Cellular Automata (TSCA) model based on empirical observations, dividing the vehicle journey over roundabouts into three stages: entrance, following, and exit. Furthermore, four optimization strategies were developed based on empirical observations and simulation results, using the traffic efficiency, delay time, and dangerous interaction frequency as key evaluation indicators. Numerical tests reveal that dangerous interactions and delays primarily occurred when the roundabout Road Occupancy Rate (ρ) ranged from 0.12 to 0.24, during which times the vehicle speed also decreased rapidly. Among the strategies, the Path Selection Based on Road Occupancy Rate Recognition Strategy (Simulation 4) demonstrated the best overall performance, increasing the traffic efficiency by 15.65% while reducing the delay time, dangerous interactions, and frequency by 6.50%, 28.32%, and 38.03%, respectively. Additionally, the Entrance Facility Optimization Strategy (Simulation 1) reduced the delay time by 6.90%. While space-based optimization strategies had a more moderate overall impact, they significantly improved the local traffic efficiency at the roundabout by approximately 25.04%. Our findings hold significant practical value, particularly with the support of onboard sensors, which can effectively detect non-compliance and provide real-time warnings to guide drivers in adhering to the prescribed traffic management strategies.

Active Inference Models of AV Takeovers: Relating Model Parameters to Trust, Situation Awareness, and Fatigue.

Our objectives were to assess the efficacy of active inference models for capturing driver takeovers from automated vehicles and to evaluate the links between model parameters and self-reported cognitive fatigue, trust, and situation awareness. Control transitions between human drivers and automation pose a substantial safety and performance risk. Models of driver behavior that predict these transitions from data are a critical tool for designing safer, human-centered, systems but current models do not sufficiently account for human factors. Active inference theory is a promising approach to integrate human factors because of its grounding in cognition and translation to a quantitative modeling framework. We used data from a driving simulation to develop an active inference model of takeover performance. After validating the model's predictions, we used Bayesian regression with a spike and slab prior to assess substantial correlations between model parameters and self-reported trust, situation awareness, fatigue, and demographic factors. The model accurately captured driving takeover times. The regression results showed that increases in cognitive fatigue were associated with increased uncertainty about the need to takeover, attributable to mapping observations to environmental states. Higher situation awareness was correlated with a more precise understanding of the environment and state transitions. Higher trust was associated with increased variance in environmental conditions associated with environmental states. The results align with prior theory on trust and active inference and provide a critical connection between complex driver states and interpretable model parameters. The active inference framework can be used in the testing and validation of automated vehicle technology to calibrate design parameters to ensure safety.

Driving Behavior Model for Multi-Vehicle Interaction at Uncontrolled Intersections Based on Risk Field Considering Drivers’ Visual Field Characteristics

Driving Behavior Model for Multi-Vehicle Interaction at Uncontrolled Intersections Based on Risk Field Considering Drivers’ Visual Field Characteristics

A deep reinforcement learning framework for achieving super-human hazard perception in autonomous driving agents

Modeling driver behavior can significantly improve autonomous vehicles and intelligent driver assistance/training systems. Hazard perception (HP) is an essential driving skill for preventing driving accidents in potentially dangerous traffic situations. HP is not a Markov decision process, so modeling HP with reinforcement learning techniques is challenging. We introduce a framework for modeling HP scenarios using the soft actor-critic approach. Then, we use this framework to model HP in the emergence of a critical lateral hazard onto the road. The dual environments – the mirror environment for training and the target environment for usage – accelerate the learning process. The agent is employed in the target environment using an interpreter algorithm. The results show that the agent learns HP and outperforms the best human results by 2.6%. The training is implemented 40 times faster in the mirror environment. Evaluation tests show a driving experience similar to real-world scenarios rather than gaming simulations.

Modeling framework of human driving behavior based on Deep Maximum Entropy Inverse Reinforcement Learning

Driving behavior modeling is extremely crucial for designing safe, intelligent, and personalized autonomous driving systems. In this paper, a modeling framework based on Markov Decision Processes (MDPs) is introduced that emulates drivers’ decision-making processes. The framework combines the Deep Maximum Entropy Inverse Reinforcement Learning (Deep MEIRL) and a reinforcement learning algorithm-proximal strategy optimization (PPO). A neural network structure is customized for Deep MEIRL, which uses the velocity of the ego vehicle, the pedestrian position, the velocity of surrounding vehicles, the lateral distance, the surrounding vehicles’ type, and the distance to the crosswalk to recover the nonlinear reward function. The dataset of drone-based video footage is collected in Xi’an (China) to train and validate the framework. The outcomes demonstrate that Deep MEIRL-PPO outperforms traditional modeling frameworks (Maximum Entropy Inverse Reinforcement Learning (MEIRL) - PPO) in modeling and predicting human driving behavior. Specifically, in predicting human driving behavior, Deep MEIRL-PPO outperforms MEIRL-PPO by 50.71% and 43.90% on the basis of the MAE and HD, respectively. Furthermore, it is discovered that Deep MEIRL-PPO accurately learns the behavior of human drivers avoiding potential conflicts when lines of sight are occluded. This research can contribute to aiding self-driving vehicles in learning human driving behavior and avoiding unforeseen risks.

A study on lane-changing risk evolution of vehicle group based on potential field model in the freeway tunnel approach section

Objective In the freeway tunnel approach section, lane-changing behaviors and transitions in the driving environment exacerbate traffic flow disruptions, increase driving risks, and lead to a higher accident rate. To this end, this study presents a method to explore the risk evolution process of lane-changing in these sections and evaluate its impact on traffic flow operations surrounding lane-changing vehicles. Methods First, a driving risk potential field model based on the field theory, which consists of a vehicle kinetic potential field and a tunnel illumination potential field, is proposed to evaluate the driving risk. Furthermore, a “vehicle group” risk graph was constructed based on graph theory, incorporating both a node-coupling driving risk model and a topological potential entropy model. Finally, trajectory datasets were collected through naturalistic driving tests to analyze the evolution of lane-changing risk and the stability of the vehicle group. Results From the analysis of coupling driving risk evolution, we found that in the [100, 500) m, [500, 1000) m, and [1000, 1500) m freeway tunnel approach sections, the coupled driving risk of lane-changing vehicle (LCV) was higher than that of the other vehicles in the vehicle group. In different tunnel approach sections, LCVs that received the highest risk were from different vehicles in vehicle group. LCVs received the highest field strength from the risk potential fields of the lateral vehicle (LV), front lateral vehicle (FLV), and front vehicle (FV) in the [100, 500) m, [500, 1000) m, [1000, 1500) m tunnel approach sections, respectively. Based on the absolute value of the vehicle group topological potential entropy, we observed the resilience of the vehicle group system improved with increasing distance from the tunnel entrance. Traffic flow regained stability more quickly after lane-changing disturbances in sections farther from the tunnel entrance. Conclusions This study highlights that the section closer to the freeway tunnel entrance significantly impact lane-changing risk, and it takes longer for the vehicle group to recover its stability after a lane-changing disturbance. The research results offer a theoretical and methodological foundation for enhancing traffic safety measures and developing microscopic driving behavior models for freeway tunnel approach sections.

A unified risk field-based driving behavior model for car-following and lane-changing behaviors simulation

The modeling of driving behavior is pivotal for the accurate simulation of traffic scenarios and for providing human-like decision-making of autonomous driving systems. Car-following (CF) and lane-changing (LC) behaviors are continuous maneuvers within traffic flow, generally modeled separately in the literature. The coherence between these two behaviors may be ignored, leading to unrealistic behavioral simulations. Therefore, this paper establishes a risk field-based driving behavior model for two-dimensional motion, ensuring coherent modeling of CF and LC behaviors under a unified framework. First, a risk quantification method is developed to calculate the risk in two-dimensional scenarios, accounting for risk over the preview time. A cubic polynomial is applied to generate path curves that align with vehicle dynamics. Second, the enhanced behavior model primarily comprises two integral components: path and trajectory planning. These two components aim to identify the path or trajectory that maximizes the benefit while meeting the desired risk. Third, the maximum acceptable risk, representing a higher risk than the desired risk, is defined to facilitate path adjustment and avoid frequent path adjustment. Finally, the proposed model is proved through comparisons with existing models using driving data. Several cases are employed for further analysis to show the model's rationality and potential in various aspects. This study develops the previous risk field-based behavior model from one-dimensional to two-dimensional scenarios, furnishes a unified framework for elucidating driving behavior in various scenarios, and contributes to the progress of behavior modeling.

Evaluation of Traffic Efficiency and Energy-Saving Benefits of L3 Smart Vehicles under the Urban Expressway Scenario

An L3 smart vehicle (L3 SV) could behave differently from human-driven vehicles due to its intelligent configuration and decision-making logic, and may consequently exert influences on traffic flow. In order to clarify the L3 SV’s traffic impacts and accelerate L3 SV implementation, this paper conducted an evaluation on the traffic efficiency and energy consumption influences of L3 SVs based on a microscopic traffic simulation. Taking Beijing as a case, the relevant traffic economic benefits were calculated with the help of the previously proposed traffic economic benefits model. Before modeling L3 SVs, a two-dimensional general modeling architecture for SVs was proposed. According to the architecture, the driving behavior model, as well as behavior selection models of L3 SVs, was eventually determined, based on which the intelligent driving model of L3 SVs was established. Urban expressways were selected as the simulation road type, and scenario analysis was conducted on various proportions of L3 SVs and L3 connected and autonomous vehicles (L3 CAVs), as well as the input traffic flow rates. It was found that L3 SVs can significantly reduce the travel time and energy consumption and enlarge the actual road capacity. The improvement will become particularly prominent under saturated or supersaturated traffic flow and increasing the proportion of L3 SVs and L3 CAVs can also amplify the effect of traffic optimization. The related economic benefit is considerable, which is CNY 3.104 billion a year based on Beijing’s travel and traffic conditions.

Driver behavior modeling at uncontrolled intersections under Indian traffic conditions

Driver behavior modeling at uncontrolled intersections under Indian traffic conditions

Data-Driven Controller for Drivers’ Steering-Wheel Operating Behaviour in Haptic Assistive Driving System

An advanced driver-assistance system (ADAS) is critical to driver–vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADASs. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving–vehicle–road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. These differences can lead to poor driving performance if not properly accounted for. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A backpropagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then, the genetic algorithm (GA) optimises the integral time absolute error (ITEA) function to produce the best multiple PID compensation parameters for various driving behaviours (such as speeding/braking, lane-keeping and turning), which are then utilised by the fuzzy logic to provide different driving commands. An experiment was conducted with five participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA proportional-integral-derivative (PID) offline, and the fuzzy-PID controller applied online. The third experiment was conducted to validate the proposed data-driven controller. The experiment and simulation results evaluated the ITAE of the lateral displacement and yaw angle during various driving behaviours. The results validated the proposed method by significantly enhancing the driving performance.

A dynamics model for driving behavior based on coupling actuation of bounded rational cognition and diverse emotions

Precise comprehension about the impacts of drivers’ rational and perceptual characteristics on their behavioral decisions is crucial for the accurate prediction of driving behavior. In the previous research on driving behavior, drivers were regarded as homogeneous and absolutely rational individuals. To overcome this limitation, the coupling effects of bounded rational cognition and diverse emotions are considered, and a driving behavior model is proposed based on Dynamics Psychology. There are two parts in this research. In Study 1, the information entropy theory is applied to describe the vehicle cluster situation, and a method is established to quantify the cognitive uncertainty of the vehicle cluster situation for drivers in diverse emotions. In Study 2, with consideration of bounded rational and emotional cognition, the impacts of the vehicle cluster situation and drivers' features, which are their demands for the driving goals including safety, efficiency and comfort, emotional states, and cognitive characteristics, on driving behavior are uniformly expressed as the behavioral driving force, and a prediction model for driving behavior is proposed based on Dynamics Psychology. The results of validation based on the virtual driving data show the prediction accuracy of the proposed model for various driving behaviors of drivers in diverse emotions is over 80%. The results of verification based on NGSIM data suggest that the prediction accuracy of the proposed model for the natural driving behaviors is 82.07%. The research results can contribute to the study on the intrinsic mechanism of driving behavior and provide theoretical support for the development of traffic simulation, personalized active safety systems, human–machine interaction, and brain-inspired autonomous driving.

Experimental Observation and Validation of EV Model for Real Driving Behavior

Experimental Observation and Validation of EV Model for Real Driving Behavior

Microscopic Modeling of Abnormal Driving Behavior: A Two-Dimensional Stochastic Formulation with Customizable Safety Levels

Microscopic Modeling of Abnormal Driving Behavior: A Two-Dimensional Stochastic Formulation with Customizable Safety Levels

Instant Inverse Modeling of Stochastic Driving Behavior With Deep Reinforcement Learning

Instant Inverse Modeling of Stochastic Driving Behavior With Deep Reinforcement Learning

Exploring Safe Overtaking Behavior on Two‐Lane Two‐Way Road Using Multiagent Driving Simulators and Traffic Simulation

Safety and efficiency of autonomous driving behavior are a tradeoff. Behaviors that are too focused on safety can reduce road operation efficiency, while those that are too efficient can compromise passengers’ safety beyond their tolerance. Therefore, it is important to understand people’s characteristics and maintain a balance between safety and efficiency. Overtaking, which involves passing the preceding vehicle and improving road capacity, requires complex interaction as collisions with opposing vehicles must be avoided on a two‐lane, two‐way road. Overtaking to increase road capacity can induce unnecessary deceleration in oncoming vehicles, harming oncoming traffic flow. To address these concerns, a diverse dataset of natural overtaking behavior is a priority. We conduct experiments using a network connection between two multiagent driving simulators to collect a human behavior‐based overtaking dataset and develop driving behavior models engaged in overtaking situations using the Extra Trees model. The behavior models are embedded in microsimulation to generate human behavior‐based datasets under different conditions using a dynamic link library and component object model interfaces. To understand the interaction in an overtaking scenario by the generated datasets, we used a K‐means clustering technique to analyze the different reaction behaviors between the oncoming and overtaking vehicles. The threshold for achieving a balanced combination of safety and efficiency is established using XGboost. Finally, safe overtaking behavior is analyzed using a combination of the classified driving styles and thresholds. The results show that the overtaking vehicle can safely start overtaking without endangering oncoming vehicles when both speed and distance conditions are met simultaneously; the speed is lower than 44.29 km/h and it is 407 m away from oncoming vehicles.

Narrow passage interactions: A UK-based exploratory survey study to identify factors affecting driver decision-making

Narrow passage interactions have received increased attention from academics seeking to create behavioural models of the interaction and those looking to define how autonomous vehicles (AVs) should interact with their human counterparts in a composite road system. Despite this increased attention, many factors remain unexplored in the narrow passage literature, with the literature also encompassing few driving culture contexts. To this end, this study employs an explorative survey to identify additional factors that affect driver decision-making during narrow passage interactions, as well as driver perceptions of different communications in a UK context. The study’s 243 participants were presented with a range of different narrow passage scenarios and asked to indicate how likely they were to give way/yield to a vehicle approaching the narrow passage from the opposite direction. In addition, they also completed the Multidimensional Driving Style Inventory to identify their driving styles and asked to identify which signals they look for from their interaction partner during narrow passage interactions, as well as the meaning of those signals. The results of the study show that situational characteristics such as the vehicle type being interacted with, being in a rush and being followed by vehicles alter the likelihood of drivers giving way at narrow passages, whilst a person’s driving style can also indicate how likely someone is to give way to another vehicle. These results highlight the factors that are considered by drivers, increasing our understanding of the factors that need to be incorporated in driver behaviour models and in AV development.

Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning

This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalised intersections. The selected samples include 2,168 observations. Almost 33% of drivers stopped ahead of the stop line, 60% passed the intersection through the yellow interval, and 7% passed after the yellow interval was complete (red light running, RLR violations). Binary logistic regression models showed that the chance of passing went up as vehicle speed went up and down as the gap between the vehicle and the traffic light and green interval went up. The movement type and vehicle position influenced the passing probability, but the vehicle type did not. Moreover, multinomial logistic regression models showed that the legal passing probability declined with the growth in the green time and vehicle distance to the traffic signal. It also increased with the growth in the speed of approaching vehicles. Also, movement type directly affected the chance of legally passing, but vehicle position and type did not. Furthermore, the driver’s performance during the yellow phase was studied using the k-nearest neighbours algorithm (KNN), support vector machines (SVM), random forest (RF) and AdaBoost machine learning techniques. The driver’s action run prediction was the most accurate, and the run-on-red camera was the least accurate.