Subject Vehicle Research Articles

Modeling discretionary lane-changing behavior on urban streets considering drivers’ heterogeneity

ABSTRACTLane changes are particularly frequent on urban streets, which not only impact traffic operations, but also cause negative effects on safety. This study uses vehicle trajectory data collected from Southwest Road in Dalian, China, to investigate the influential factors of discretionary lane changing in an urban road environment. Both the standard logit model and mixed logit model were fitted to the data, which evidently identified several key factors. The mixed logit model outperformed the standard logit model in terms of model fit. The results suggest that driver heterogeneity is present on the speed differentials of the subject vehicle and the leading vehicles, and the distance gap in the target lane. A sensitivity analysis was further conducted to quantify the degree of influence of the statistically significant variables. The findings support the notion and purpose of discretionary lane changing, i.e. seeking a speed advantage and/or a more satisfactory driving environment.

Effects of on-Board Unit on Driving Behavior in Connected Vehicle Traffic Flow

Connected vehicle technology has potentials to increase traffic safety, reduce traffic pollution, and ease traffic congestion. In the connected vehicle environment, the information interaction among people, cars, roads, and the environment is significantly enhanced, and driver behavior will change accordingly due to increased external stimulation. This paper designed a Vehicle-to-Vehicle (V2V) on-board unit (OBU) based on driving demand. In addition, a simulation platform for the interconnection and communication between the OBU and simulator was built. Thirty-one test drivers were investigated to drive an instrumented vehicle in four scenarios, with and without the OBU under two different traffic states. Collected trajectory data of the subject vehicle and the vehicle in front, as well as sociodemographic characteristics of the test drivers were used to evaluate the potential impact of such OBUs on driving behavior and traffic safety. Car-following behavior is an essential component of microsimulation models. This paper also investigated the impacts of the V2V OBU on car-following behaviors. Considering the car-following related indicators, the k-Means algorithm was used to categorize different car-following modes. The results show that the OBU has a positive impact on drivers in terms of speed, front distance, and the time to stable regime. Furthermore, drivers’ opinions show that the system is acceptable and useful in general.

Unbalanced Multiple Left Turn Lane Usage Modelling: From Individual Choice to Aggregate Volume

Diverse lane preferences of left-turn drivers lead to unbalanced traffic distribution on multiple left-turn lanes. The preferences can be measured in terms of lane usage at macroscopic level and individual lane choice at microscopic level. The data of lane volume and individual lane choices are collected at eight dual or triple left-turn lanes equipped in signalized intersections in China. Linear regression model with dummy variables and discrete choice model were applied to analyse drivers’ lane choosing patterns at macroscopic and microscopic levels, respectively, and results of the two studies are mutually verified and complemented. The drivers’ lane preferences are found to vary with approach configurations, traffic control, and the number of lanes available. Static influential factors, such as turning radius inside the intersection, the design of shadowed lane, and intersection skewedness, as well as dynamic influential factors, including queue length, heavy vehicle in queue back and subject vehicle type, could affect the drivers’ lane preferences. The findings of this study have important implications for intersection design and traffic control in practice.

The Roles of Car Following and Lane Changing Drivers’ Anticipations during Vehicle Inserting Process: A Structural Equation Model Approach

Anticipating ability is a skill that drivers count on to handle risky tasks in the traffic. This paper explores how the drivers of lane changing vehicle and its immediately car follower anticipate surrounding vehicles’ movements and adjust their manoeuvers during vehicle inserting process. The drivers’ anticipating mechanisms are modelled in the framework of structural equation model and estimated from field data. Results show that the change of lane changing type or traffic signal affects the drivers’ anticipation. Increased vehicle speed impels subject driver to anticipate driving condition in further future, but the stimulus is lower than the one coming from the kinematic comparisons of subject vehicle and other vehicles. The drivers care more about the vehicles’ interactions with which they are personally involved than the one to which they are only onlookers. The drivers’ responses to the counterpart vehicle’s movements depend on the progress of vehicle insertion and their roles in vehicle interactions.

A Comparison Analysis of Surrogate Safety Measures with Car-Following Perspectives for Advanced Driver Assistance System

Surrogate Safety Measure (SSM) is one of the most widely used methods for identifying future threats, such as rear-end collision. Various SSMs have been proposed for the application of Advanced Driver Assistance Systems (ADAS), including Forward Collision Warning System (FCWS) and Emergency Braking System (EBS). The existing SSMs have been mainly used for assessing criticality of a certain traffic situation or detecting critical actions, such as severe braking maneuvers and jerking before an accident. The ADAS shows different warning signals or movements from drivers’ driving behaviours depending on the SSM employed in the system, which may lead to low reliability and low satisfaction. In order to explore the characteristics of existing SSMs in terms of human driving behaviours, this study analyzes collision risks estimated by three different SSMs, including Time-To-Collision (TTC), Stopping Headway Distance (SHD), and Deceleration-based Surrogate Safety Measure (DSSM), based on two different car-following theories, such as action point model and asymmetric driving behaviour model. The results show that the estimated collision risks of the TTC and SHD only partially match the pattern of human driving behaviour. Furthermore, the TTC and SHD overestimate the collision risk in deceleration process, particularly when the subject vehicle is faster than its preceding vehicle. On the other hand, the DSSM shows well-matched results to the pattern of the human driving behaviour. It well represents the collision risk even when the preceding vehicle moves faster than the follower one. Moreover, unlike other SSMs, the DSSM shows a balanced performance to estimate the collision risk in both deceleration and acceleration phase. These research findings suggest that the DSSM has a great potential to enhance the driver’s compliance to the ADAS, since it can reflect how the driver perceives the collision risks according to the driving behaviours in the car-following situation.

Influence of oncoming traffic on drivers’ overtaking of cyclists

Active transportation - such as cycling - can provide health benefits to the population. However, cycling safety remains a major threat to favour the use of bicycles and, for this reason, more efforts are needed to reduce the number of crashes involving cyclists. One crash scenario which deserves special attention is driver’s overtaking of cyclists since it has an increased likelihood to lead to severe injuries. During the overtaking manoeuvre, the monitoring of subjective risk can influence the decision-making process and lead to different outcomes. In this context, the present driving simulator study aims to investigate how the time to collision between oncoming traffic and subject vehicle affected the overtaking strategy, and the minimum safety margins towards the overtaken cyclist. The results show that a decrease in time to collision against the oncoming vehicle significantly affects the drivers’ overtaking strategy (accelerative vs. flying), inducing more drivers to choose an accelerative overtaking manoeuvre. The decrease in time to collision also produces a decrease in minimum safety margins to the cyclists for drivers who opt for a flying overtaking strategy. Finally, the current research shows that the minimum lateral safety margins were smaller and the mean speed higher in flying manoeuvres compared to accelerative manoeuvres. Overall, the combination of lower safety margins and higher mean speeds in flying overtaking manoeuvres seems to pose a risk for cyclists’ safety. The findings of the study provide some implications for the design of automated driving.

Conclusive Review on Adaptive Headlight System for Four-Wheeler Frontlight using Stepper Motor

Major road mishaps occur at night on account of curve roads and glare caused from the headlights of incoming vehicles. Night time driving with conventional headlamps is particularly unsafe: only 25% of the driving is done at night but 55% of the driving accidents occur during this period. The existing conventional light system does not provide illumination in the right direction and at the precise angle. Due to this constrain, a need to understand an alternative technology solution. Adaptive front lighting system (AFS) helps improve driver’s visibility at night time hence achieving enhance safety. The objective of this work is to design and build an AFS Prototype. From the results, it is concluded that the headlamp swings in horizontal direction by sensing steering angle and vertical by sensing distance between subject vehicle and next vehicle. Accuracy, reliability and availability of the components were few considerations during the conceptualization stage.

Conclusive Review on Adaptive Headlight System for Four-Wheeler Frontlight using Stepper Motor

Major road mishaps occur at night on account of curve roads and glare caused from the headlights of incoming vehicles. Night time driving with conventional headlamps is particularly unsafe: only 25% of the driving is done at night but 55% of the driving accidents occur during this period. The existing conventional light system does not provide illumination in the right direction and at the precise angle. Due to this constrain, a need to understand an alternative technology solution. Adaptive front lighting system (AFS) helps improve driver’s visibility at night time hence achieving enhance safety. The objective of this work is to design and build an AFS Prototype. From the results, it is concluded that the headlamp swings in horizontal direction by sensing steering angle and vertical by sensing distance between subject vehicle and next vehicle. Accuracy, reliability and availability of the components were few considerations during the conceptualization stage.

Modeling Driver Merging Behavior: A Repeated Game Theoretical Approach

Various lane-changing models have been developed for use within microscopic traffic simulation software to replicate driver merging behavior. An understanding of human driving behavior, which can be gained through such modeling, will be critical in harmonizing emerging advanced vehicle technology, such as connected automated vehicles, with human drivers. Therefore, it is important to ensure that lane-changing models are clearly understood, appropriately designed, and carefully calibrated. An earlier study by Kang and Rakha proposed and developed a decision-making model for merging maneuvers using a game theoretical approach considering two drivers: the driver of the subject vehicle (DS) in an acceleration lane and the driver of the following lag vehicle (DL) in the target lane. The previous model assumed that the DS and DL decide on an action at the first point only, where the subject and lag vehicles are identified. The current study extends the Kang and Rakha model by introducing the concept of a repeated game, assuming that a lane change decision is made repeatedly to adjust to changes in surrounding conditions. For example, drivers often decide to change their initial decision as a result of conflicts with other drivers. A repeated game helps the proposed model produce more realistic decision-making in the lane-changing process. To evaluate the model, driver decisions at a certain stage, along with accumulated historical decision data, were extracted from Next Generation SIMulation (NGSIM) data. The validation results reveal that the proposed repeated game model produces considerable prediction accuracy (above 75%).

Application of Trajectory Data for Investigating Vehicle Behavior in Mixed Traffic Environment

The research work reported here investigates driving behavior under mixed traffic conditions on high-speed, multilane highways. With the involvement of multiple vehicle classes, high-resolution trajectory data is necessary for exploring vehicle-following, lateral movement, and seeping behavior under varying traffic flow states. An access-controlled, mid-block road section was selected for video data collection under varying traffic flow conditions. Using a semi-automated image processing tool, vehicular trajectory data was developed for three different traffic states. Micro-level behavior such as lateral placement of vehicles as a function of speed, instant responses, vehicle-following behavior, and hysteresis phenomenon were evaluated under different traffic flow states. It was found that lane-wise behavior degraded with increase in traffic volume and vehicles showed a propensity to move towards the median at low flow and towards the curb-side at moderate and heavy flows. Further, vehicle-following behavior was also investigated and it was found that with increase in flow level, vehicles are more inclined to mimic the leader vehicle’s behavior. In addition to following time, perceiving time of subject vehicle for different leading vehicles was also evaluated for different vehicle classes. From the analysis, it was inferred that smaller vehicles are switching their leader vehicles more often to escape from delay, resulting in less following and perceiving time and aggressive gap acceptance. The present research work reveals the need for high-quality, micro-level data for calibrating driving behavior models under mixed traffic conditions.

Robust lane tracking algorithm for forward target detection of automated driving vehicles

Detection of a primary forward target is one of the most important factors in a longitudinal control system of automated driving vehicles. If there is no special event such as a lane change, a lane detected by a vision sensor can be deemed as a predicted driving path of the ego vehicle. Based on this, it is possible to determine the state of the primary forward target vehicle accurately using the detected lanes. However, malfunction of vision sensors can induce unrecognized and misrecognized lane detection problems on account of internal/external environment factors. Furthermore, if a detection point is getting farther from the subject vehicle, the detection accuracy is getting lower because of inaccuracy of lane model parameters. To solve these problems, a novel method of lane tracking has been investigated. First, an integrated sensor module that combines a virtual sensor and a vision sensor has been developed. The virtual sensor is combined kinematic and dynamic vehicle model to be used in the full-speed range. Second, a lane estimator to improve lane detection accuracy at a long distance has been developed. The lane width in the public road can be assumed to be constant in the same road type. Based on this assumption, the clothoid parameters can be restored and consequently improve the lane detection accuracy. The performance of the proposed algorithm was verified by actual vehicle tests on public roads with manual driving and on proving ground with an automated driving system. The proposed algorithm has been compared with a conventional method which is based on in-vehicle yaw rate sensor. The test results have shown significant improvement of lane tracking performance over the conventional method.

Effects of hands-free cellular phone conversational cognitive tasks on driving stability based on driving simulation experiment

Driver distraction due to cellular phone usage is a major contributing factor to road crashes. This study compares the effects of conversational cognitive tasks using hands-free cellular phone on driving performance under three distraction conditions: (1) no distraction (no cellular conversation), (2) normal conversation (non-emotional cellular conversation), and (3) seven-level mathematical calculations. A car-following scenario was implemented using a driving simulator. Thirty young drivers with an average age of 24.1 years maintained a constant speed and distance between the subject vehicle and a leading vehicle on the driving simulator, and then respond to the leading vehicle’s emergency stop. The driving performances were assessed by collecting and statistically analyzing several variables of maneuver stability: the drivers’ brake reaction times, driving speed fluctuation, car-following distance undulation, and car-following time-headway undulation. The results revealed that normal conversation on a hands-free cellular phone impaired driving performance. The degree of impairment caused by normal calculation was equivalent to the distraction caused by Level 3 mathematical calculations according to the seven-level calculation baseline. The calculation difficulty of Level 3 is one double-digit figure plus a single-digit figure, and non-carry addition mental arithmetic is required, e.g., 44 + 4. The results indicated that an increase in the level of complexity of the calculation task was associated with an increase in brake reaction time. The seven-level calculation-task baseline could be applied to measure additional distraction effects on driving performance for further comparison.

Stochastic Model-Predictive Control for Lane Change Decision of Automated Driving Vehicles

This paper describes lane change motion planning with a combination of probabilistic and deterministic prediction for automated driving under complex driving circumstances. The autonomous lane change should arrive safely at the destination. The subject vehicle needs to perceive and predict the behaviors of other vehicles with sensors. From the information of other vehicles, a collision probability is defined using a reachable set of uncertainty propagation. In addition, the lane change risk is monitored using predicted time-to-collision and safety distance to guarantee safety in lane change behavior. A safe driving envelope is defined as constraints based on the combinatorial prediction (probabilistic and deterministic) of the behavior of surrounding vehicles. To obtain the desired steering angle and longitudinal acceleration to maintain the automated driving vehicle under constraints, a stochastic model-predictive control problem is formulated. The proposed model has been evaluated by performing lane change simulations in MATLAB/Simulink, while considering the effect of combination prediction. Also, the proposed algorithm has been implemented on a test vehicle. The simulation and test results show that the proposed algorithm can handle complicated lane change scenarios, while guaranteeing safety.

Effect of Gap Acceptance Behavior of the Right Turning Vehicles on the Major Road Stream for Uncontrolled Three-Legged Intersections under Mixed Traffic Conditions

Uncontrolled intersections are the intersections where there are no external signs or signals to control the movement of vehicles. In mixed traffic conditions priority rules are often violated by the road users. In All-way-stop-controlled intersections (AWSC), the vehicle should stop themselves before they enter the intersection and should check whether any vehicles are present in the other approaches of the intersection. If no vehicle is present, then they can cross the intersection. For this study, data were collected from two uncontrolled three-legged intersections located at various parts of India and critical gap required for each vehicle combination to cross the intersection are extracted. Gap Acceptance method is used for mixed traffic condition because it is based on the critical gap and follow-up time, which in turn depends on the type of vehicles and traffic conditions. This study tries to analyze the effect of vehicle type on gap acceptance behaviour at uncontrolled three-legged intersection. From this study, it is observed that size of the vehicles and traffic volumes has an influence on the critical gap. Depending on the major road vehicle type combinations, the critical gap for each right turning subject vehicle varied from minimum of 1.4 s to a maximum of 8.7s.

Functional perspective-based probabilistic fault detection and diagnostic algorithm for autonomous vehicle using longitudinal kinematic model

This paper describes a functional perspective-based probabilistic fault detection and diagnostic algorithm of an autonomous vehicle using a longitudinal kinematic model. The relative displacement and velocity between the subject vehicle and a preceding vehicle was obtained by a radar installed in front of the autonomous vehicle. The obtained relative values were used to control the longitudinal behavior of the autonomous vehicle, the longitudinal acceleration of which was obtained from an internal sensor. In order to detect and diagnose actual faults in the obtained values, such as relative displacement, velocity, and acceleration, a fault detection and diagnostic algorithm for the longitudinal control of the autonomous vehicle was developed using a sliding mode observer and predictive function. The probabilistic analysis of fault signals was conducted using the constructed sliding mode observer and predictive function. The actual driving data of the vehicle preceding the subject vehicle was used for the rational performance evaluation of the proposed algorithm. The performance evaluation was conducted in the MATLAB/SIMULINK environment. The evaluation results showed that the proposed fault detection and diagnostic algorithm can stochastically detect and diagnose applied fault signals.

On actions of long combination vehicle drivers prior to lane changes in dense highway traffic – A driving simulator study

In this paper we address drivers’ actions prior to mandatory lane changes of long combination vehicles in dense highway traffic. The studied driver actions were: turn indicator activation, speed reduction and lateral intrusion. We categorised and compared the drivers’ actions with respect to the surrounding traffic cooperation and the level of urgency. Urgency here was based on the remaining distance to a targeted exit ramp. The results show that when the subject vehicle is close to the exit ramp, drivers used speed reduction significantly more than when the vehicle is further away. No significant difference was found for the use of lateral intrusion considering the distance to the exit ramp. As regards traffic cooperation, significant differences were found for both speed reduction and lateral intrusion. The drivers’ speed reduction and lateral intrusion were significantly greater when the surrounding traffic cooperation was low.

Comparisons of mandatory and discretionary lane changing behavior on freeways

This research performs comparative analyses on drivers’ behavior during mandatory and discretionary lane changes. We do this by examining the statistical properties of four lane changing decision variables that describe the gaps between the subject vehicle and the surrounding vehicles. Mandatory and discretionary lane changes in NGSIM’s I-80 Freeway and U.S. Highway 101 data collection sites were identified. First, for each variable at the same site, descriptive statistics for the two types of lane changes were compared, and hypothesis tests on the difference between two means were conducted. Then, for each decision variable at the same site, the observed cumulative distributions between the mandatory and discretionary lane changes were compared by means of the Kolmogorov–Smirnov test. This test was repeated for the fitted distributions of the same decision variable at the same site. The results show that, for the three decision variables associated with gaps in the target lane, the means and distributions between the two types of lane changes are not significantly different. The only variable found to have significant differences in means and distributions is the gap between the subject vehicle and the preceding vehicle in the original lane. This may be because this variable is not an important input in mandatory lane change decisions. This finding provides statistical justification for researchers to develop models with different inputs for mandatory and discretionary lane changes in driver assist systems, in autonomous vehicles, and in microscopic traffic simulation tools.

Assessment of driver compliance on roadside safety signs with auditory warning sounds generated from pavement surface–a driving simulator study

Abstract A variety of word messages are used in highways in different forms to inform drivers of traffic safety information or to influence positively drivers' behavior. These include direct word messages for a particular event (such as road work) or general safety messages that warn drivers of risky driving behaviors (such as distracted driving and speeding). However, it is often observed that many drivers even do not recognize the safety messages despite being displayed on roadside signs in a fairly good visibility condition. The present study focused on an engineering method, namely auditory warning sound (AWS), which calls driver's attention on driving tasks and helps them comply with roadside safety signs. A driving simulator experiment was conducted to assess effects of AWS on driver compliance to roadside safety signs. AWS was implemented into driving simulator scenarios as a parameter to generate a certain level of growling warning sounds when subject vehicles are entering within a legibility distance of a roadside safety sign. The present study described laboratory setup and data for the driving simulator experiment, and drew conclusions on driver compliance to roadside safety signs with and without presence of AWS. The experiment results show that drivers are more compliant to roadside safety signs when AWS is used. It is expected that AWS will greatly help drivers comply with roadside safety signs where a specific safety concern is raised, such as a work-zone or a drowsy driving advisory zone.

Multi Vehicle-Type Right Turning Gap-Acceptance and Capacity Analysis at Uncontrolled Urban Intersections

Intersections are the critical zones where conflicting, merging and diverging movements influence the intersection capacity. Uncontrolled intersections in particular pose dangerous situations to vehicular traffic. During peak vehicular flow, the unpredictable crossing behavior of minor stream vehicles induces delay and reduces the capacity of the intersection. Capacity at uncontrolled intersections is typically measured either by gap acceptance method, empirical regression approaches and conflict technique. Gap acceptance is an important characteristic for analyzing uncontrolled intersections. The behavior of different vehicle types and gap of subject vehicle type from minor street taking right turn to merge with major traffic stream is analyzed using gap acceptance method. The objective of the current study is to analyze the effect of major stream vehicle type combinations on the minor stream vehicle gap-acceptance behavior and to determine the capacity of the minor stream taking into account the influence of the right turning vehicles. The capacity of minor stream calculated using Highway Capacity Manual (HCM) 2010, Luttenin’s model, and Tanner’s model are compared. It is observed that two wheelers are more aggressive than three wheelers for most of the major stream vehicular combinations observed in this study.

Development of a lane change risk index using vehicle trajectory data

Surrogate safety measures (SSMs) have been widely used to evaluate crash potential, which is fundamental for the development of effective safety countermeasures. Unlike existing SSMs, which are mainly focused on the evaluation of longitudinal vehicle maneuvering leading to rear-end crashes, this study proposes a new method for estimating crash risk while a subject vehicle changes lanes, referred to as the lane change risk index (LCRI). A novel feature of the proposed methodology is its incorporation of the amount of exposure time to potential crash and the expected crash severity level by applying a fault tree analysis (FTA) to the evaluation framework. Vehicle interactions between a subject vehicle and adjacent vehicles in the starting lane and the target lane are evaluated in terms of crash potential during lane change. Vehicle trajectory data obtained from a traffic stream, photographed using a drone flown over a freeway segment, is used to investigate the applicability of the proposed methodology. This study compares the characteristics of compulsory and discretionary lane changes observed in a work zone section and a general section of a freeway using the LCRI. It is expected that the outcome of this study will be valuable in evaluating the effectiveness of various traffic operations and control strategies in terms of lane change safety.