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

An Integrated Critical Information Delivery Platform for Smart Segment Dissemination to Road Users

An integrated critical information delivery platform for smart segment dissemination to road users was developed. A statewide baseline milepost geodatabase was created at 0.1-mile resolution with tools, protocols, and interfaces that allow other data sources to be efficiently utilized. A variety of data sources (e.g., INRIX, CARS, Doppler, camera images, connected vehicle data, automated vehicle location) were integrated into existing and new dashboards for stakeholders to monitor roadway conditions and after-action reviews. Additionally, based on these data sources, algorithms were developed and an API was created to identify hazardous road conditions when the location of the end-user mobile device was given. Message delivery schemes were successfully implemented to issue alerts to drivers, which were integrated with two in-vehicle smartphone applications. The performance of the integrated platform was evaluated using both the driving simulator and a number of simulated and on-road tests. The results demonstrated the system was able to disseminate data in real-time using the developed platform

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

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

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

Space advanced technology demonstration satellite

The Space Advanced Technology demonstration satellite (SATech-01), a mission for low-cost space science and new technology experiments, organized by Chinese Academy of Sciences (CAS), was successfully launched into a Sun-synchronous orbit at an altitude of similar to 500 km on July 27, 2022, from the Jiuquan Satellite Launch Centre. Serving as an experimental platform for space science exploration and the demonstration of advanced common technologies in orbit, SATech-01 is equipped with 16 experimental payloads, including the solar upper transition region imager (SUTRI), the lobster eye imager for astronomy (LEIA), the high energy burst searcher (HEBS), and a High Precision Magnetic Field Measurement System based on a CPT Magnetometer (CPT). It also incorporates an imager with freeform optics, an integrated thermal imaging sensor, and a multi-functional integrated imager, etc. This paper provides an overview of SATech-01, including a technical description of the satellite and its scientific payloads, along with their on-orbit performance

Skeleton model based behavior recognition for pedestrians and cyclists from vehicle sce ne camera

With the significant advances in computer vision research, skeleton model based human pose recognition has become more accurate and time-efficient, although most of the applications are limited in laboratory environment or on surveillance videos. This paper proposes a pose tracking and behavior recognition method from in-vehicle scene camera. It will not only detect pedestrians on the road, but also generate their skeleton models describing head, limb, and trunk movements. Based on these more detailed movements of body parts, the proposed method is designed to track poses of pedestrians and cyclists with the potentials to enable automated pedestrian gesture reading and non-verbal interactions between autonomous vehicles and pedestrians. The proposed algorithm has been tested on different databases including TASI 110-car naturalistic driving database and Joint Attention for Autonomous Driving (JAAD) database. Results show that key frames describing different pedestrian and cyclist negotiation gestures are detected from the raw video streams using the proposed method. These results will improve our understanding of pedestrian and cyclist's intentions and can be further used for autonomous vehicle control algorithm development