scholarly journals A Crash Injury Model Involving Autonomous Vehicle: Investigating of Crash and Disengagement Reports

2021 ◽  
Vol 13 (14) ◽  
pp. 7938
Author(s):  
Amolika Sinha ◽  
Vincent Vu ◽  
Sai Chand ◽  
Kasun Wijayaratna ◽  
Vinayak Dixit
Keyword(s):  
Autonomous Vehicles ◽  
Performance Metrics ◽  
Autonomous Vehicle ◽  
Machine Learning Techniques ◽  
Motor Vehicles ◽  
Crash Severity ◽  
Contributing Factors ◽  
California Department ◽  
The Usa ◽  
Report Data

Autonomous vehicles (AVs) are being extensively tested on public roads in several states in the USA, such as California, Florida, Nevada, and Texas. AV utilization is expected to increase into the future, given rapid advancement and development in sensing and navigation technologies. This will eventually lead to a decline in human driving. AVs are generally believed to mitigate crash frequency, although the repercussion of AVs on crash severity is ambiguous. For the data-driven and transparent deployment of AVs in California, the California Department of Motor Vehicles (CA DMV) commissioned AV manufacturers to draft and publish reports on disengagements and crashes. This study performed a comprehensive assessment of CA DMV data from 2014 to 2019 from a safety standpoint, and some trends were discerned. The results show that decrement in automated disengagements does not necessarily imply an improvement in AV technology. Contributing factors to the crash severity of an AV are not clearly defined. To further understand crash severity in AVs, the features and issues with data are identified and discussed using different machine learning techniques. The CA DMV accident report data were utilized to develop a variety of crash AV severity models focusing on the injury for all crash typologies. Performance metrics were discussed, and the bagging classifier model exhibited the best performance among different candidate models. Additionally, the study identified potential issues with the CA DMV data reporting protocol, which is imperative to share with the research community. Recommendations are provided to enhance the existing reports and append new domains.

Extracting Rules from Autonomous-Vehicle-Involved Crashes by Applying Decision Tree and Association Rule Methods

2021 ◽  
pp. 036119812110184
Author(s):  
Md Tanvir Ashraf ◽  
Kakan Dey ◽  
Sabyasachee Mishra ◽  
Md Tawhidur Rahman
Keyword(s):  
Decision Tree ◽  
Association Rule ◽  
Autonomous Vehicles ◽  
Human Error ◽  
Autonomous Vehicle ◽  
Motor Vehicles ◽  
Operational Mode ◽  
Contributing Factors ◽  
California Department ◽  
Transportation Agencies

Autonomous vehicles (AVs) can dramatically reduce the number of traffic crashes and associated fatalities by eliminating the avoidable human-error-related crash contributing factors. Many companies have been conducting pilot tests on public roads in several states in the U.S. and other countries to accelerate AV mass deployment. AV pilot operations on Californian public roads saw 251 AV-involved crashes (as of February 2020). These AV-involved crashes provide a unique opportunity to investigate AV crash risks in the mixed traffic environment. This study collected the AV crash reports from the California Department of Motor Vehicles and applied the decision tree and association rule methods to extract the pre-crash rules of AV-involved crashes. Extracted rules revealed that the most frequent types of AV crashes were rear-end crashes and predominantly occurred at intersections when AVs were stopped and engaged in the autonomous mode. AV and non-AV manufacturers and transportation agencies can use the findings of this study to minimize AV-related crashes. AV companies could install a distinct signal/display to inform the operational mode of the AVs (i.e., autonomous or non-autonomous) to human drivers around them. Moreover, the automatic emergency braking system in non-AVs could avoid a significant number of rear-end crashes as, often, rear-end crashes occurred as a result of the failure of following non-AVs to slow down in time behind AVs. Transportation agencies can consider separating AVs from non-AVs by assigning “AV Only” lanes to eliminate the excessive rear-end crashes resulting from the mistakes of human drivers in non-AVs at intersections.

scholarly journals Crash and disengagement data of autonomous vehicles on public roads in California

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Amolika Sinha ◽  
Sai Chand ◽  
Vincent Vu ◽  
Huang Chen ◽  
Vinayak Dixit
Keyword(s):  
Autonomous Vehicles ◽  
Road Network ◽  
Research Community ◽  
Motor Vehicles ◽  
Primary Data ◽  
Disruptive Technology ◽  
California Department ◽  
Crash Data ◽  
Contributory Factors ◽  
The Usa

AbstractAutonomous Vehicles (AVs) are being widely tested on public roads in several countries such as the USA, Canada, France, Germany, and Australia. For the transparent deployment of AVs in California, the California Department of Motor Vehicles (CA DMV) commissioned AV manufacturers to draft and publish reports on disengagements and crashes. These reports must be processed before any statistical analysis, which is cumbersome and time-consuming. Our dataset presents the processed disengagement data from 2014 to 2019, crash data till the 10th of March 2020 and supplementary road network and land-use data extracted from OpenStreetMap. Primary data are manually assessed and converted into an easily processed format. Our processed data will be advantageous to the research community and enable accelerated research in this domain. For example, the data can be utilised to discern trends in disengagement, observe the distribution of disengagement causes, and investigate the contributory factors of the crashes. Such investigations can subsequently improve the reporting protocols and make policies and laws for the smooth deployment of this disruptive technology.

scholarly journals Analysis of Factors Affecting the Severity of Automated Vehicle Crashes Using XGBoost Model Combining POI Data

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hengrui Chen ◽  
Hong Chen ◽  
Zhizhen Liu ◽  
Xiaoke Sun ◽  
Ruiyu Zhou
Keyword(s):  
Classification And Regression Tree ◽  
Motor Vehicles ◽  
Gradient Boosting ◽  
Crash Severity ◽  
Contributing Factors ◽  
Land Use Patterns ◽  
California Department ◽  
Model Combining ◽  
Collision Type ◽  
Extreme Gradient Boosting

The research and development of autonomous vehicle (AV) technology have been gaining ground globally. However, a few studies have performed an in-depth exploration of the contributing factors of crashes involving AVs. This study aims to predict the severity of crashes involving AVs and analyze the effects of the different factors on crash severity. Crash data were obtained from the AV-related crash reports presented to the California Department of Motor Vehicles in 2019 and included 75 uninjured and 18 injured accident cases. The points-of-interest (POI) data were collected from Google Map Application Programming Interface (API). Descriptive statistics analysis was applied to examine the features of crashes involving AVs in terms of collision type, crash severity, vehicle movement preceding the collision, and degree of vehicle damage. To compare the classification performance of different classifiers, we use two different classification models: eXtreme Gradient Boosting (XGBoost) and Classification and Regression Tree (CART). The result shows that the XGBoost model performs better in identifying the injured crashes involving AVs. Compared with the original XGBoost model, the recall and G-mean of the XGBoost model combining POI data improved by 100% and 11.1%, respectively. The main features that contribute to the severity of crashes include weather, degree of vehicle damage, accident location, and collision type. The results indicate that crash severity significantly increases if the AVs collided at an intersection under extreme weather conditions (e.g., fog and snow). Moreover, an accident resulting in injuries also had a higher probability of occurring in areas where land-use patterns are highly diverse. The knowledge gained from this research could ultimately contribute to assessing and improving the safety performance of the current AVs.

scholarly journals Road Accident Reconstruction Using On-board Data, Especially Focusing on the Applicability in Case of Autonomous Vehicles

2020 ◽  
Author(s):  
Krisztian Pinter ◽  
Zsolt Szalay ◽  
Gabor Vida
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Road Accident ◽  
Event Data ◽  
The European Union ◽  
Passenger Cars ◽  
Event Reconstruction ◽  
Before And After ◽  
The Usa ◽  
Data Points

The application of Event Data Recorder (EDR) in passenger cars and vans has been compulsory in the USA since 2014. In the European Union, every passenger car and vehicle manufactured and released must have e-call systems since April 2018. However, neither the data recorded in Event Data Recorders regulated by the current standards nor the data recovered from e-call systems are enough to reconstruct the movements of the vehicle before and after the accident to a degree that the accident could be analyzed in the perspective of liability. The continuous expansion of autonomous vehicle functions – which will inevitably lead to completely autonomous vehicles – makes it particularly justifiable that all vehicles should possess EDR functions and that these data recorders shall store the satisfactory number of parameters for the vehicle's full movement reconstruction.In the article, we will present a process of defining a data package – which will include a definition process for both the data points and the frequency of measuring and recording – that enables the post-event reconstruction of the full motion process, the vehicle movements and the evaluation of liability issues in both regular and irregular operation of autonomous and partially autonomous vehicles.

Risk Analysis of Autonomous Vehicles in Mixed Traffic Streams

2017 ◽  
Vol 2625 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Parth Bhavsar ◽  
Plaban Das ◽  
Matthew Paugh ◽  
Kakan Dey ◽  
Mashrur Chowdhury
Keyword(s):  
Traffic Safety ◽  
Failure Probability ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Fault Tree ◽  
Communication Technologies ◽  
Transportation Infrastructure ◽  
New Combination ◽  
Motor Vehicles ◽  
Mixed Traffic

The introduction of autonomous vehicles in the surface transportation system could improve traffic safety and reduce traffic congestion and negative environmental effects. Although the continuous evolution in computing, sensing, and communication technologies can improve the performance of autonomous vehicles, the new combination of autonomous automotive and electronic communication technologies will present new challenges, such as interaction with other nonautonomous vehicles, which must be addressed before implementation. The objective of this study was to identify the risks associated with the failure of an autonomous vehicle in mixed traffic streams. To identify the risks, the autonomous vehicle system was first disassembled into vehicular components and transportation infrastructure components, and then a fault tree model was developed for each system. The failure probabilities of each component were estimated by reviewing the published literature and publicly available data sources. This analysis resulted in a failure probability of about 14% resulting from a sequential failure of the autonomous vehicular components alone in the vehicle’s lifetime, particularly the components responsible for automation. After the failure probability of autonomous vehicle components was combined with the failure probability of transportation infrastructure components, an overall failure probability related to vehicular or infrastructure components was found: 158 per 1 million mi of travel. The most critical combination of events that could lead to failure of autonomous vehicles, known as minimal cut-sets, was also identified. Finally, the results of fault tree analysis were compared with real-world data available from the California Department of Motor Vehicles autonomous vehicle testing records.

scholarly journals Simulation-Based Performance Analysis for a Novel AVS/RS Technology with Movable Lifts

2021 ◽  
Vol 11 (5) ◽  
pp. 2283
Author(s):  
Boris Jerman ◽  
Banu Yetkin Ekren ◽  
Melis Küçükyaşar ◽  
Tone Lerher
Keyword(s):  
Autonomous Vehicles ◽  
Performance Metrics ◽  
Autonomous Vehicle ◽  
Arrival Rate ◽  
Point Location ◽  
Throughput Rate ◽  
Storage And Retrieval ◽  
Simulation Based ◽  
Initial Work ◽  
Novel Design

This paper studies a novel autonomous vehicle-based storage and retrieval system (AVS/RS) design with movable lifts (AVS/RS/ML). In the proposed system, there are aisle-captive lifts that are able to travel along the warehouse aisle to position themselves at the target column location. Those lifts can lift up/down the autonomous vehicles to/from the target storage compartment when they are in standstill. This novel design is proposed as an alternative to existing AVS/RSs to balance the resource utilizations as well as to provide an inexpensive solution with highly utilized autonomous vehicles (i.e., AGVs). As an initial work, for this novel system, two alternative operating designs under different racking configurations are experimented. We compare those two designs by their throughput rate performance metrics under the arrival rate scenarios with highly utilized AGVs (i.e., 95%). Besides, we experiment with two warehouse capacity scenarios: 900 and 1800 storage compartments. The results show that designs with two separate I/O point locations provide a better throughput rate than designs with single I/O point location. Besides, a decreased number of columns in the system improves the system’s performance.

Autonomous Vehicles: A Perspective from the California Department of Motor Vehicles

2014 ◽  
pp. 15-24 ◽  
Author(s):  
Bernard C. Soriano ◽  
Stephanie L. Dougherty ◽  
Brian G. Soublet ◽  
Kristin J. Triepke
Keyword(s):  
Autonomous Vehicles ◽  
Motor Vehicles ◽  
California Department

Is thyroid surgery performed too often in Germany?

2015 ◽  
Vol 54 (03) ◽  
pp. 101-105 ◽  
Author(s):  
F. A. Verburg
Keyword(s):  
Surgical Procedures ◽  
Thyroid Surgery ◽  
Needle Biopsy ◽  
Fine Needle Biopsy ◽  
Contributing Factors ◽  
Fine Needle ◽  
Diagnostic Measures ◽  
Frequent Use ◽  
Preoperative Diagnostic ◽  
The Usa

SummaryThyroid surgery is one of the more common surgical procedures in Germany. This is in contrast with the situation in some other countries, where this procedure is performed comparatively rarely. In this paper the number of thyroid surgeries in Germany is compared with other western countries (Netherlands, USA, England). In contrast to e. g. the USA and England the number of thyroid surgeries in Germany is declining, however with approximately 109/100 000/year in 2012 is still elevated (Netherlands: 16/100 000/year, USA: at least 42/100 000/year, England: at least 27/100 000/year).Possible contributing factors to this higher number of thyroid surgeries in Germany are explored. These factors include iodine deficiency, the frequent use of advanced diagnostics such as ultrasound, insufficient use of preoperative diagnostic measures such as fine needle biopsy and the practice of “defensive medicine”. How much each of these factors contributes is however unclear.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

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