Vehicle-Pedestrian Dynamic Interaction through Tractography of Relative Movements and Articulated Pedestrian Pose Estimation

To design robust Pre-Collision Systems (PCS) we must develop new techniques that will allow a better understanding of the vehicle-pedestrian dynamic relationship, and which can predict pedestrian future movements. This paper focuses on the potential-conflict situations where a collision may happen if no avoidance action is taken from driver or pedestrian. We have used 1000 15-second videos to find vehicle-pedestrian relative dynamic trajectories and pose of pedestrians. Adaptive structural local appearance model and particle filter methods have been implemented to track the pedestrians. We have obtained accurate tractography results for over 82% of the videos. For pose estimation, we have used flexible mixture model for capturing cooccurrence between pedestrian body segments. Based on existing single-frame human pose estimation model, we have implemented Kalman filtering with other new techniques to make stable stickfigure videos of the pedestrian dynamic motion. These tractography and pose estimation data were used as features to train a neural network for classifying 'potential conflict' and 'no potential conflict' situations. The training of the network achieved 91.2% true label accuracy, and 8.8% false level accuracy. Finally, the trained network was used to assess the probability of collision over time for the 15 seconds videos which generates a spike when there is a 'potential conflict' situation. The paper enables new analysis on potential-conflict pedestrian cases with 2D tractography data and stick-figure pose representation of pedestrians, which provides significant insight on the vehicle-pedestrian dynamics that are critical for safe autonomous driving and transportation safety innovations

Back of Queue Warning and Critical Information Delivery to Motorists

Back-of-queue crashes are one of the main sources for fatal accidents on U.S. highways. A variety of factors including low visibility, slippery road surface, and driver distraction/drowsiness during highway cruising, all contribute to this type of fatal crashes. Thus, it is very important to improve the driver’s situational awareness before they approach traffic queues on highways. In this project, we develop a prototype in-vehicle back-of-queue alerting system that is based on the probe vehicle data from INDOT. Speed changes among different road segments are used to identify slow traffic queues, which are compared with vehicle locations and moving directions to detect potential back-of-queue crashes. This prototype system is designed to issue alerting messages to drivers approaching the highway traffic queues via an Android-based smartphone app and an Android Auto device. The performance of this system has been evaluated using the driving simulator and a limited number of on-road test runs. The results showed the effectiveness and benefits of this prototype system

Pedestrian/Bicyclist Limb Motion Analysis from 110-Car TASI Video Data for Autonomous Emergency Braking Testing Surrogate Development

Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions. We modeled the pedestrian/bicyclist limb motions in four layers: (1) the percentages of pedestrians and bicyclists who have limb motions when crossing the road; (2) the averaged action frequency and the corresponding distributions on when there are limb motions; (3) comparisons of the limb motion behavior between crossing and non-crossing cases; and (4) the effects of seasons on the limb motions when the pedestrians/bicyclists are crossing the road. The results of this study can provide empirical foundations supporting surrogate development, benefit analysis, and standardized testing of vehicular pedestrian/bicyclist detection and crash mitigation systems

Risk assessment and mitigation of e-scooter crashes with naturalistic driving data

Recently, e-scooter-involved crashes have increased significantly but little information is available about the behaviors of on-road e-scooter riders. Most existing e-scooter crash research was based on retrospectively descriptive media reports, emergency room patient records, and crash reports. This paper presents a naturalistic driving study with a focus on e-scooter and vehicle encounters. The goal is to quantitatively measure the behaviors of e-scooter riders in different encounters to help facilitate crash scenario modeling, baseline behavior modeling, and the potential future development of in-vehicle mitigation algorithms. The data was collected using an instrumented vehicle and an e-scooter rider wearable system, respectively. A three-step data analysis process is developed. First, semi-automatic data labeling extracts e-scooter rider images and non-rider human images in similar environments to train an e-scooter-rider classifier. Then, a multi-step scene reconstruction pipeline generates vehicle and e-scooter trajectories in all encounters. The final step is to model e-scooter rider behaviors and e-scooter-vehicle encounter scenarios. A total of 500 vehicle to e-scooter interactions are analyzed. The variables pertaining to the same are also discussed in this paper

Peek into the Future Camera-based Occupant Sensing in Configurable Cabins for Autonomous Vehicles

The development of fully autonomous vehicles (AVs) can potentially eliminate drivers and introduce unprecedented seating design. However, highly flexible seat configurations may lead to occupants' unconventional poses and actions. Understanding occupant behaviors and prioritize safety features become eye-catching topics in the AV research frontier. Visual sensors have the advantages of cost-efficiency and high-fidelity imaging and become more widely applied for in-car sensing purposes. Occlusion is one big concern for this type of system in crowded car cabins. It is important but largely unknown about how a visual-sensing framework will look like to support 2-D and 3-D human pose tracking towards highly configurable seats. As one of the first studies to touch this topic, we peek into the future camera-based sensing framework via a simulation experiment. Constructed representative car-cabin, seat layouts, and occupant sizes, camera coverage from different angles and positions is simulated and calculated. The comprehensive coverage data are synthesized through an optimization process to determine the camera layout and overall occupant coverage. The results show the needs and design of a different number of cameras to fully or partially cover all the occupants with changeable configurations of up to six seats.Comment: Conference: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) Link: https://ieeexplore.ieee.org/document/956442

Alternate Interchange Signing Study for Indiana Highways

The main objectives of this research were to (1) understand signing issues from the perspective of drivers and (2) develop recommendations for improving interchange signing in Indiana to aid driver understanding and increase the safety and efficiency of highway traffic operations. An online survey with specific questions was designed and distributed through email, social media, online newspapers, and a survey company with the goal of better understanding driver thinking when approaching decision-making areas on the interstate. The analysis of the survey results revealed the following. Drivers usually do not know the interchange types as they approach an interchange on the freeway. Drivers are most interested in which lanes they should be in when approaching an interchange, even in advance of typical signing locations. Drivers do not like signs that require cognitive work since it will delay their driving decision by creating uncertainty. Different drivers need different types of information from signs, such as cardinal direction, destination name, road name, and lane assignments. Therefore, a perfect sign for one driver may be confusing or information overload for another driver. In some instances, a driver who is familiar with the area is confused by the signs because the sign information contradicts the driver’s knowledge

A Wearable Data Collection System for Studying Micro-Level E-Scooter Behavior in Naturalistic Road Environment

As one of the most popular micro-mobility options, e-scooters are spreading in hundreds of big cities and college towns in the US and worldwide. In the meantime, e-scooters are also posing new challenges to traffic safety. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than the pedestrains and bicyclists. These features make e-scooters challenging for human drivers, pedestrians, vehicle active safety modules, and self-driving modules to see and interact. To study this new mobility option and address e-scooter riders' and other road users' safety concerns, this paper proposes a wearable data collection system for investigating the micro-level e-Scooter motion behavior in a Naturalistic road environment. An e-Scooter-based data acquisition system has been developed by integrating LiDAR, cameras, and GPS using the robot operating system (ROS). Software frameworks are developed to support hardware interfaces, sensor operation, sensor synchronization, and data saving. The integrated system can collect data continuously for hours, meeting all the requirements including calibration accuracy and capability of collecting the vehicle and e-Scooter encountering data.Comment: Conference: Fast-zero'21, Kanazawa, Japan Date of publication: Sep 2021 Publisher: JSA

Assessing the Effectiveness of In-Vehicle Highway Back-of-Queue Alerting System

This paper proposes an in-vehicle back-of-queue alerting system that is able to issue alerting messages to drivers on highways approaching traffic queues. A prototype system was implemented to deliver the in-vehicle alerting messages to drivers via an Android-based smartphone app. To assess its effectiveness, a set of test scenarios were designed and implemented on a state-of-the-art driving simulator. Subjects were recruited and their testing data was collected under two driver states (normal and distracted) and three alert types (no alerts, roadside alerts, and in-vehicle auditory alerts). The effectiveness was evaluated using three parameters of interest: 1) the minimum Time-to-Collision (mTTC), 2) the maximum deceleration, and 3) the maximum lateral acceleration. Statistical models were utilized to examine the usefulness and benefits of each alerting type. The results show that the in-vehicle auditory alert is the most effective way for delivering alerting messages to drivers. More specifically, it significantly increases the mTTC (30% longer than that of 'no warning') and decreases the maximum lateral acceleration (60% less than that of 'no warning'), which provides drivers with more reaction time and improves driving stability of their vehicles. The effects of driver distraction significantly decrease the efficiency of roadside traffic sign alert. More specifically, when the driver is distracted, the roadside traffic sign alert performs significantly worse in terms of mTTC compared with that of normal driving. This highlights the importance of the in-vehicle auditory alert when the driver is distracted

Validity and reliability of dynamic virtual interactive design methodology

This study focuses on testing the validity and reliability of dynamic Virtual Interactive Design (VID) methodology with dynamic ergonomics analysis. Virtual Interactive Design methodology has been introduced and applied on practical problems in several previous studies, and initially validated with posture-based static ergonomics analysis tools. Although most results have proved the validity and reliability based on static information considered, such validation processes is not sufficient since risks for performing certain tasks can not be fully examined without examining dynamic aspects. But the dynamic virtual interactive design environment has not been validated sufficiently. In my subsequent study, a dynamic ergonomics analysis tool will be integrated into virtual interactive design environment. For the validation of new dynamic virtual interactive design environment, experimental human motion data from 36 subjects in several tasks are imported into the integrated system and dynamic analysis results are achieved. Also, dynamic ergonomics risk results from motion captured directly from human subjects and static ergonomics risk results from virtual interactive design environment are calculated, which two will be used as standard. Comparisons between interested motion series and standard series with respect to ergonomics risk results are applied for validation purpose. And test-retest method is used for testing reliability

Effect of work complexity & individual differences on nursing IT utilization

Various healthcare IT systems have been developed to reduce medication errors. Although these systems can help to improve patient safety and reduce adverse medical events, new problems are also generated with their utilizations. One key problem during IT implementation is the change of working process. Although many of these changes are recorded in different industries, very limited studies discuss the effects of working process features on IT implementation results. Especially in the healthcare IT implementation area, there are few researchers comprehensively studying how the design or features of working process may affect IT utilization. Along the medication administration process, the majority of healthcare IT related studies focus on earlier stages like drug ordering, transcribing, and dispensing, and more researches are needed to focus on administration and monitoring stages. Considering the medication administration and monitoring work in inpatient department is mostly completed by nurses, in this study, the computerized medication administration system for nurses is the targeted healthcare IT system, and the nursing work process in inpatient departments is the target work environment. Research questions in this study include: (1) how to model the work process of nursing work and study its effects on nursing IT utilization, (2) how to model the user cognition process after implementation when the IT system use is mandatory, and (3) what measures of individual differences of the nurses should be considered as factors affecting the nursing IT utilization. The nursing work complexity is firstly proposed in this study by combining information from sociotechnical nursing work system, detailed nursing work contents and healthcare working features. The nursing work complexity construct not only can represent the features of nursing work process, but also have better individual sensitivity than the work process. In this study, the nursing IT utilization is defined as continued use of IT system during and after IT implementation. The first stage is the period during IT implementation where Technology Acceptance Model related classic models can be directly applied. The second stage is the period long time after IT implementation where a new model is proposed based on IS continuance theory. In both IT utilization stages, the effects of nursing work complexity on IT satisfaction are studied and compared. Also, effects of some individual differences of nurses on IT utilization are studied in these two stages including gender, age, IT-experience, perceived innovative of IT system, shift, practice, nursing work experience, and emotional intelligence. Data are collected both during the IT implementation and long time after. The results show that nursing work complexity can be measured using the proposed list of items in this study. The effects of nursing work complexity on IT satisfaction are significant both during and long time after IT implementation. But during IT implementation, perceived work performance is one significant mediator between nursing work complexity and IT satisfaction. One IT continuance model is proposed and validated to model the cognitive process of users for continued use of IT system. Nursing work complexity and emotional intelligence are proved to have some types of direct effects on this IT continuance model. For the simple variables, practice is proved to have significant effects on nursing attitudes towards the IT system during implementation, and age and position of nurses are proved to be significant moderators towards the IT continued use