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Fatal injuries (1327) Injuries (683) Crashes (465) Vehicle drivers (346) Vehicle occupants (282) Roads and highways (194) Safety belts (173) Frontal collisions (137) Airbag systems (132) Occupant protection (120) Research and development (119) Side impact crashes (114) Head injuries (110) Impact tests (109) Rollover accidents (101) Statistical analysis (92) Technical review (90) Trucks (90) Restraint systems (81) Children (68) Test procedures (66) Accident reconstruction (62) Crashworthiness (62) Education and training (57) Torso (56) Head (55) Body regions (51) Injury causation (49) Accident types (48) Crash statistics (48) Light trucks (45) Anthropometric test devices (44) Nervous system (43) Two or three wheeled vehicles (43) Adults (41) Computer simulation (41) Helmets (40) Evacuation and escape (38) Heavy trucks (38) Child restraint systems (34) Regulations (34) Simulation and modeling (34) Fluids and secretions (33) Legislation (33) Pedestrian injuries (33) Commercial vehicles (32) Aircraft (31) Biomechanics (31) Seats and seating (31) Buses (30)

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Does forever safety exist in L3 driving?

  • Guangzhou Automobile Group Co., Ltd. - Jiaxin Ke
  • Guangzhou Automobile Group Co Ltd - ZHENNAN WANG, Guoyang Xie
  • Show More
  • Technical Paper
  • 2018-01-1607
Published 2018-08-07 by SAE International in United States
Recently, Level 3 (L3) autonomous driving system has caught more and more attention and car companies, like Audi, Tesla, are racing towards bringing the L3 system into mass production. Among all the issues, reliability and safety problems have become hot topics in the L3 driving system. Without the guarantee of safety, the autonomous cars cannot be accepted by the public. Nowadays safety strategy is judged by how much information gathered on the road. However, such data based safety strategy cannot lower the fatality rate to a degree that is accepted by the public due to the huge amount of data required. As a result, a model based safety strategy is required. By formalize human reactions into models, the autonomous cars’ behavior is more understandable to other human drivers and as a result, provide a more comfortable driving experience. Besides, by carefully designing the model, the autonomous car will not be blamed for actions being taken and provide explainable clues to determine the responsibility for accidents that are caused by other drivers. Among all the L3…

Controlling LED Based Adaptive Front-Lighting System Using Machine Learning

  • Tata Elxsi, Ltd. - Sivaprasad Nandyala, Sriharsha Santhapur, Kshitij Kumar, Mithun Manalikandy
Published 2018-04-03 by SAE International in United States

Accidents in nights have a major share in all automotive accidents. Even though, the average distance driven in dark is 75% less as compared to the average distance driven during the day, the fatalities in nights due to road accidents are 300% of the day time. Again, the statistical studies from the National Safety Council disclose the fact that 55% of all road accidents in nights occur at the curved roads due to insufficient illumination and poor judgment of curves. The paper proposes a control algorithm with machine learning that controls LED matrix headlamp to provide precise beam pattern shaping and beam intensity (i.e. high and low beam). The system is designed to give the driver improved visibility under varying driving conditions. Adaptive Front Lighting System is an intelligent system, designed in MATLAB\Simulink environment that optimizes the illumination of roads during the night, on the basis of inputs from different sensors in the vehicle.

Roadside Boundaries and Objects for the Development of Vehicle Road Keeping Assistance System

  • Indiana University, Purdue University - Dan Shen, Qiang Yi, Jun Lin, Renran Tian, Stanley Chien
  • Toyota Motor North America Inc. - Rini Sherony
Published 2018-04-03 by SAE International in United States

Road departure is a leading cause of fatal crashes in the US and half of all the crashes are related to road departure [1]. Road departure warning (RDW) and road keeping assistance (RKA) are the new active safety areas to be explored. Most of the currently available road-departure detection technologies rely on the detection of lane markings, which are either missing or unclear in many roads. Therefore, in additional to the these lane markings, next-generation road departure detection should rely on the detection of other road edge and boundary objects. Common road edge and boundary indicators include lane marking, grass, curb, metal guardrail, concrete divider, traffic barrels and cones. This paper investigates the distribution of major types of road edges and road boundaries in the United States in order to enhance and evaluate the capabilities and effectiveness of RDW and RKA. The paper describes the road location sample used for the analysis, presents the percentage of various types roadside objects in terms of number of appearance locations, percentage miles (%miles), and percentage car-miles (%car-miles = %miles*car_density). The percentage of roads that do not have any lane marking and do not have clear lane marking is also described. The representative shapes of each type of roadside objects are studied. The result is applicable for the development and evaluation of road departure warning and road keeping assistance systems.

Characteristics and Casualty Analysis of Two- Wheeler Accidents in China, Data Source: The China In-Depth Accident Study (CIDAS)

  • CATARC - Qiang Chen, Bing Dai
Published 2018-04-03 by SAE International in United States

The two-wheeler is a vehicle that runs on two wheels, which is classified as motorcycle, electric-bicycle, and bicycle in this research. China has the largest number of two-wheelers and relevant accidents in the world. The two-wheeler riders have a high level of vulnerability, creating a significant necessity to better understand the characteristics according to the road-user group. The objective of this paper is to study the characteristics and analyze the causes of two-wheeler accidents in China using the CIDAS (China In-Depth Accident Study) Database. 2012 cases of two-wheeler accidents with riders injured or dead were collected from the CIDAS Database from 5 cities (Changchun, Beijing, Weihai, Ningbo and Foshan) in China over a period of 5 years (2011.07-2016.06). Several key parameters such as accident characteristics, accident scenarios (containing collision point distribution, accident type, clock direction distribution and head WAD distribution) were analyzed using measurement and mathematical statistical methods. Results show that the motorcycle accident was the most frequent two-wheeler accident type in China, which accounted for 52.7% of the total two-wheeler accidents. The two-wheeler riders’ injury information were also queried from the CIDAS Database for detailed study regarding injuries and their sources. The results of the analysis allow for an overall assessment of the two-wheeler riders safety level in China, thus providing a useful support to decision makers working to improve the protection of two-wheeler riders from fatal accidents by a series of countermeasures.

Driver Response Time to Midblock Crossing Pedestrians

  • University of Guelph - Ryan Toxopeus
  • Kodsi Engineering - Shady Attalla, Sam Kodsi
  • Show More
Published 2018-04-03 by SAE International in United States

Vehicle-pedestrian collisions account for 15% of fatal crashes in the USA, and there has been a twelve percent increase in fatal crashes in the USA from 2006 to 2015. Although research exists on the response time of drivers responding to pedestrian path intrusions, data on the response time of through drivers to jaywalking pedestrians crossing from the far side of the road has not been determined. Therefore, the purpose of this study was to quantify Driver Response Time (DRT) to a pedestrian that intrudes perpendicularly into the path of a vehicle from the far curb (adjacent to oncoming traffic). 50 (NFemale = 25; NMale = 25) licensed volunteer drivers took part in a study at the University of Guelph Driving Research in Virtual Environments (DRiVE) lab using an Oktal complete vehicle driving simulator. After a brief practice drive to acclimatize to the virtual environment, participants completed the approximately 10 minute experiment drive during which the pedestrian hazard was presented. Only eight percent of drivers collided with the pedestrian with a mean time-to-impact of 4.35 seconds. There were no gender differences in terms of DRT or crash rate.

Suspension Health Monitoring and Failure Prognosis Through Onboard SoC and Cloud Based Reporting

  • KPIT Technologies, Ltd. - Naveen Manuel, Jainendra Mishra
Published 2018-04-03 by SAE International in United States

Failures of automotive mechanical systems such as suspension systems, or “springs” while a vehicle is in operation is most often serious, and can sometimes incur financial loses or even fatal consequences. Spring failures result from either chronic overloading, poor driver behavior, severe duty cycle, or a combination of the aforementioned conditions. These conditions result in extraordinary fatigue, ultimately reducing the spring’s overall expected useful life. There has been a significant reduction in the cost of onboard computing making it economically viable to measure, record, and store various parameters that affect the spring life. An economical measurement system was designed that could plot the Load vs. Displacement graph (L/D) by simply measuring the spring’s displacement from its nominal (static) position. Previous methodologies have used expensive load cells; in our demonstration we prove that measurements can be taken much more economically using an array of Hall Effect type sensors. The L/D plots are stored and compared and a performance deterioration curve is plotted and an acute failure timeline is predicted, information of which is constantly relayed to the cloud. Alarm flags are raised at the manufacturer’s/supplier’s front-end by an intuitive app and further actions may be planned. An onboard display can be used to inform driver about its haul-mass, optimum speed bracket to be maintained and even inform about regular checkup deadlines. Hence, when implemented, the technology can be useful for failure prognosis by OEMs, tier ones, service agencies and Insurance Agencies.

Introduction to Traffic Signal Data Loggers and their Application to Accident Reconstruction

  • Focus Forensics - Jay Przybyla, Thomas Rush, Kelly Palframan, Daniel Melcher
Published 2018-04-03 by SAE International in United States

Each year in the United States, approximately 1 million collisions occur at signalized intersections, representing over 15% of all collisions and almost 9% of traffic fatalities. Engineers seeking to understand the roadway, vehicular, and driver factors related to these collisions are often asked to investigate and assess the traffic signal timing, right of way issues, and the signal indications displayed to involved drivers during the period of time leading up to and including the impact events. Until relatively recently, investigators were limited by the absence of any recording devices within the systems used for traffic signal phasing and timing. Accident reconstruction methods have long relied on the generalized signal phasing and timings programmed for that intersection by the responsible jurisdiction, combined with the vehicle dynamics calculated for the collision sequence in conjunction with witness testimony regarding signal indications and phase changes. Recent technological advancements in signal timing data collection, recording, and logging can provide engineers and investigators with a new, time-specific, incident-relatable and more robust method of analyzing signalized intersection collisions. This paper presents the current state of the art for traffic signalization: Signal Timing Data Loggers. This paper also presents how to obtain, analyze, and interpret the data logger data and possible applications of such data to accident reconstruction.

Driver Response Time to Cyclist Path Intrusions

  • University of Guelph - Ryan Toxopeus
  • Kodsi Engineering - Shady Attalla, Sam Kodsi
  • Show More
Published 2018-04-03 by SAE International in United States

Motor vehicle crashes with cyclists are on the rise, with a six percent increase in fatal crashes from 2006 to 2015 in the USA. Although some research exists on the response time of drivers to some types of path intrusions, data on the perception-response of through drivers to cyclists who fail to stop at a stop sign, and ride into the path of the vehicle has not been researched. The purpose of this study was to quantify the Driver Response Time (DRT) to a cyclist that intrudes perpendicularly in front of a through vehicle at an intersection where the driver has the right-of-way. The DRT was measured from when the cyclist is positioned at the stop sign until the driver reacts, whether by touching the brake pedal, swerving (steering wheel angle change of at least 2 degrees), accelerating, or a combination of those responses.

Design and Implementation of a Hybrid Fuzzy-Reinforcement Learning Algorithm for Driver Drowsiness Detection Using a Driving Simulator

  • K. N. Toosi University of Technology - Armin Kassemi Langroodi, Ali Nahvi
  • Journal Article
  • 02-11-01-0005
Published 2018-03-08 by SAE International in United States

Driver drowsiness is the cause of many fatal accidents all over the world. Many research works have been conducted on detecting driver drowsiness for more than half a century, but statistical data show that such accidents have not decreased significantly. Most researchers have focused on using certain sensors and extracting their relevant features. However, there has been no research work on developing an algorithm to detect driver drowsiness independently from the input type. In this paper, a hybrid fuzzy-reinforcement learning drowsiness detection algorithm is presented. This algorithm is flexible to work with any number and any kind of data related to driver alertness. It estimates the level of alertness based on an arbitrary number of inputs. The algorithm extracts driving patterns specific to each driver and determines driver’s level of drowsiness using a continuous numerical variable rather than a discrete variable. To evaluate the algorithm, only six features related to only steering wheel angle and velocity are used. The accuracy of the user-specific data is 81.1% validated with the Observer Rating of Drowsiness criterion. This hybrid fuzzy-reinforcement learning algorithm has 46.4% improvement over the artificial neural network user-specific dataset method, and 49.2% over the artificial neural network general dataset method. The results can be improved even further if we use more features related to the driver and the vehicle.

ZED Connect brings mobile-based ELD to improve safety and service

  • Matthew Borst
  • Magazine Article
  • 17TOFHP12_07
Published 2017-12-01 by SAE International in United States

One of the most-pressing topics for the trucking industry this year has been the new Federal Motor Carrier Safety Administration's (FMCSA) rule requiring the use of electronic logging devices (ELD) in commercial vehicles involved in interstate commerce. Drivers currently using paper logs or logging software have until December 18, 2017, to comply, while drivers using automatic onboard recording devices (AOBRD) get an extended deadline to December 2019. Recognizing this new reality, Cummins created a separate company called ZED Connect that would provide an open data platform for customers across the industry to utilize.