Renran Tian

Studying the Effects of Driver Distraction and Traffic Density on the Probability of Crash and Near-Crash Events in Naturalistic Driving Environment

Driver distraction detection and intervention are important for designing modern driver-assistance systems and for improving safety. The main research question of this paper is to investigate how the cumulative driver off-road glance duration can be controlled to reduce the probability of occurrences of crash and near-crash events. Based on the available data sets from the Virginia Tech Transportation Institute (VTTI) 100-car study, the conditional probability is calculated to study the chance of crash and near-crash events when the given cumulative off-road glance duration in 6 s has been reached. Different off-road eye-glance locations and traffic density levels are also evaluated. The results show that one linear relationship can be obtained between the cumulative off-road eye-glance duration in 6 s and the risk of occurrences of crash and near-crash events, which varies for different off-road eye-glance locations. In addition, the traffic density level is found to be one significant moderator to this linear relationship. Detailed comparisons are made for different traffic density levels, and one nonlinear equation is obtained to predict the probability of occurrences of crash and near-crash events by considering both cumulative off-road glance duration and traffic density levels.

Estimation of the vehicle-pedestrian encounter/conflict risk on the road based on TASI 110-car naturalistic driving data collection

Modeling vehicle-pedestrian interactions in the road environment is essential to develop pedestrian detection and pedestrian crash avoidance systems. In this paper, one novel approach is proposed to estimate the vehicle-pedestrian encountering risk in the road environment based on a large scale naturalistic driving data collection. Considering the difficulty to record actual pedestrian crashes in the naturalistic data collection, the encountering risk is estimated by the chances for driver to meet with pedestrian in the roadway as well as the chances for the driver and pedestrian to get into a potential conflict. Effects of different scenarios consisting of road conditions, pedestrian behaviors, and pedestrian numbers on the risk levels are also evaluated, and significant results are provided.

Pilot study on pedestrian step frequency in naturalistic driving environment

Investigation of pedestrian step frequency is essential for analyzing walking gaits and pedestrian behaviors. However, most research about step frequency is performed in labs or manually controlled experimental environment, which greatly limits the utilization of the results to analyze and/or predict real pedestrian behaviors. This study investigates the step frequencies of pedestrian in naturalistic driving environment. The mean step frequency values and distribution are studied in all cases and separately for road crossing cases only. Furthermore, comparisons of pedestrian step frequencies are made considering three different impact factors. The results have shown that in real world, people tend to use higher step frequencies when crossing the road, especially when the vehicle is moving towards the pedestrian or when the pedestrians are crossing without right-of-way.

Study on the Display Positions for the Haptic Rotary Device-Based Integrated In-Vehicle Infotainment Interface

Integrated multimodal systems is one promising direction to improve human-vehicle interaction. In order to create intelligent human-vehicle interfaces and reduce visual load during secondary tasks, combining a haptic rotary device and a graphic display will provide one practical solution. However, in literature, the proper display position for the haptic rotary device is not fully investigated. In this paper, one experimental infotainment system is studied (including a haptic rotary control device and a graphic display) to evaluate the proper display position. Measurements used include task completion time, reaction to road events, lane/velocity keeping during secondary tasks, and user preference. Three display positions are considered: high mounted position, cluster position, and center stack position. The results show that, with increased on-road and off-road visual loads, the cluster display position can reduce lane position deviation significantly compared to high mounted and center stack positions. In addition, the high mounted and cluster display positions are better toward two different road events, including strong wind gust and extreme deceleration of the lead car.

Connecting Road Environment Features and Driver Glance Behavior in the Macro Level: Surrounding Vehicle Patterns, Traffic Density, and Driver Eye-glance Behaviors

Although integration of environment and driver information can be achieved at both micro- and macro- levels with different benefits towards driving safety, most studies focus only on the micro-level integration by coupling individual external environment events and driver responses. In the macro level, however, it is more important to understand overall effects of environment features on driver behavior, and their combined effects on driving safety. Based on some previous findings on the significant effects of driver glance behavior on crash risk and the prominent moderating effects of traffic density levels on this relationship, this paper tries to use surrounding vehicle patterns to classify traffic density levels and study the direct effects of traffic density levels on driver glance behavior. The datasets used for analysis are based on VTTI 100-car study. After proposing the measures of surrounding vehicle patterns, the classification of traffic density is completed using support vector machine (SVM). The results show that, although it is difficult to classify four traffic density levels based on the proposed surrounding vehicle patterns, the predication accuracy for two or three traffic density levels is good. Also, significant effects of traffic density on driver glance behaviors to the vehicle mirrors are identified.

Single-Variable Scenario Analysis of Vehicle-Pedestrian Potential Crash Based on Video Analysis Results of Large-Scale Naturalistic Driving Data

Vehicle-pedestrian crashes are big concerns in transportation safety, and it is important to study the vehicle-pedestrian crash scenarios in order to facilitate the development and evaluation of pedestrian crash mitigation systems. Many researchers have tried to investigate the pedestrian crash scenarios relying on crash databases or pedestrian behavior prediction models, both of which have some limitations like limited generalizability of the results, missing of important information, biased results. In this study, we propose to study the potential crash scenarios as one surrogate targets of the actual pedestrian crash scenarios. Extended from several previous studies, one single-variable scenario analysis is completed based on the video analysis results of one large-scale naturalistic driving data collection focusing on recording pedestrian behaviors in all kinds of situations. Through calculating potential conflict rates and applying chi-square tests for around 40 attributes from 12 scenario variables individually, this study has found out that number of pedestrians, pedestrian moving speed, pedestrian moving direction, vehicle moving direction, road type, road location, and existence of road separator/median are all important scenario variables for potential pedestrian-vehicle crashes.

A Universal 3D Gait Planning Based on Comprehensive Motion Constraints

To realize stable walking and complex motion in the 3D unstructured environment, a parametric universal gait planning is proposed with the consideration of boundary constraints, physical constraints and ZMP stability constraints of locomotion. This approach adopts a spline-based parametric method to simplify the complicated joint trajectory planning problem, and converts it to a constrained optimization problem of the parametric vector. With different gait parameters and boundary constraints, this approach was extended to more complex gait planning. As examples, three different gaits were generated, including start walking, stop walking and kicking a ball.