scholarly journals Automated Driving System and Property Damage Liability Insurance

2020 ◽  
Vol 2020 (651) ◽  
pp. 651_25-651_50
Author(s):  
Kyoko Kaneoka
Keyword(s):  
Liability Insurance ◽  
Automated Driving ◽  
Property Damage ◽  
Driving System

scholarly journals Driver intervention performance assessment as a key aspect of L3–L4 automated vehicles deployment

2021 ◽  
Vol 11 (1) ◽  
pp. 845-852
Author(s):  
Aleksandra Rodak ◽  
Paweł Budziszewski ◽  
Małgorzata Pędzierska ◽  
Mikołaj Kruszewski
Keyword(s):  
Performance Assessment ◽  
Minimal Risk ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Driving System ◽  
Driving Task ◽  
Driving Conditions

Abstract In L3–L4 vehicles, driving task is performed primarily by automated driving system (ADS). Automation mode permits to engage in non-driving-related tasks; however, it necessitates continuous vigilance and attention. Although the driver may be distracted, a request to intervene may suddenly occur, requiring immediate and appropriate response to driving conditions. To increase safety, automated vehicles should be equipped with a Driver Intervention Performance Assessment module (DIPA), ensuring that the driver is able to take the control of the vehicle and maintain it safely. Otherwise, ADS should regain control from the driver and perform a minimal risk manoeuvre. The paper explains the essence of DIPA, indicates possible measures, and describes a concept of DIPA framework being developed in the project.

When the automated driving system fails: Dynamics of public responses to automated vehicles

2021 ◽  
Vol 129 ◽  
pp. 103271
Author(s):  
Zhigang Xu ◽  
Zijun Jiang ◽  
Guanqun Wang ◽  
Runmin Wang ◽  
Tingting Li ◽  
...  
Keyword(s):  
Automated Vehicles ◽  
Automated Driving ◽  
Driving System

In Pursuit of Emergency Procedures for Automated Driving System-Involved Scenarios

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Travis Terry ◽  
Tammy E. Trimble ◽  
Mindy Buchanan-King ◽  
Myra Blanco ◽  
Vikki L. Fitchett ◽  
...  
Keyword(s):  
Automated Driving ◽  
Driving System ◽  
Emergency Procedures

scholarly journals Improving Usefulness of Automated Driving by Lowering Primary Task Interference through HMI Design

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Frederik Naujoks ◽  
Yannick Forster ◽  
Katharina Wiedemann ◽  
Alexandra Neukum
Keyword(s):  
Driving Simulator ◽  
Primary Task ◽  
Automated Driving ◽  
Task Interference ◽  
Driving System ◽  
Lane Changes ◽  
Speech Output ◽  
Driving Time ◽  
Standard Information ◽  
Machine Interface

During conditionally automated driving (CAD), driving time can be used for non-driving-related tasks (NDRTs). To increase safety and comfort of an automated ride, upcoming automated manoeuvres such as lane changes or speed adaptations may be communicated to the driver. However, as the driver’s primary task consists of performing NDRTs, they might prefer to be informed in a nondistracting way. In this paper, the potential of using speech output to improve human-automation interaction is explored. A sample of 17 participants completed different situations which involved communication between the automation and the driver in a motion-based driving simulator. The Human-Machine Interface (HMI) of the automated driving system consisted of a visual-auditory HMI with either generic auditory feedback (i.e., standard information tones) or additional speech output. The drivers were asked to perform a common NDRT during the drive. Compared to generic auditory output, communicating upcoming automated manoeuvres additionally by speech led to a decrease in self-reported visual workload and decreased monitoring of the visual HMI. However, interruptions of the NDRT were not affected by additional speech output. Participants clearly favoured the HMI with additional speech-based output, demonstrating the potential of speech to enhance usefulness and acceptance of automated vehicles.

scholarly journals For Preventative Automated Driving System (PADS): Traffic Accident Context Analysis Based on Deep Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1829
Author(s):  
Minhee Kang ◽  
Jaein Song ◽  
Keeyeon Hwang
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Deep Neural Networks ◽  
Time Of Day ◽  
Automated Driving ◽  
Context Analysis ◽  
Driving System ◽  
Vehicle Type ◽  
Fast Development ◽  
Day Of The Week

Automated Vehicles (AVs) are under development to reduce traffic accidents to a great extent. Therefore, safety will play a pivotal role to determine their social acceptability. Despite the fast development of AVs technologies, related accidents can occur even in an ideal environment. Therefore, measures to prevent traffic accidents in advance are essential. This study implemented a traffic accident context analysis based on the Deep Neural Network (DNNs) technique to design a Preventive Automated Driving System (PADS). The DNN-based analysis reveals that when a traffic accident occurs, the offender’s injury can be predicted with 85% accuracy and the victim’s case with 67%. In addition, to find out factors that decide the degree of injury to the offender and victim, a random forest analysis was implemented. The vehicle type and speed were identified as the most important factors to decide the degree of injury of the offender, while the importance for the victim is ordered by speed, time of day, vehicle type, and day of the week. The PADS proposed in this study is expected not only to contribute to improve the safety of AVs, but to prevent accidents in advance.

scholarly journals “Estimating incurred claims”

1969 ◽  
Vol 5 (2) ◽  
pp. 274-279
Author(s):  
V. Benedikt ◽  
Herbert L. Feay
Keyword(s):  
United States ◽  
The United States ◽  
Liability Insurance ◽  
Insurance Company ◽  
Property Damage ◽  
Bodily Injury

Mr. Benedikt uses “chain relatives” based on the incurred claim totals included in Part 5 of Schedule “P” of the annual statement required for fire and casualty companies in the United States. Each total is for the losses as developed to end of calendar year (j) for claims incurred because of accidents in calendar year (i). Each total is the sum of the actual payments made before the end of year (j) plus the reserve for estimated payments to be made after the end of year (j) for claims incurred in year (i). The “chain relatives” are ratios. The “chain relative” ai,j is the ratio of developed losses to end of (j + 1) to the developed losses at the end of year (j).Each total of Part 5 of Schedule “P” equals the sum of the total payments to date plus the total reserves for future payments for the corresponding classification of claims. Separate totals for these amounts are given in Part 1 of Schedule “P”. The totals of Part 5 are not secured directly from Part 1 because Part 1 gives totals by policy year of issue only and Part 5 separates the totals by policy of issue by calendar year in which claims are incurred. The two parts are prepared from the same basic claim information and agree in total.The accumulated total paid losses for most casualty lines increase with passage of time. This accumulated total for paid losses can be reduced only if there are recoveries for losses previously paid, such as can occur for auto collision. For auto collision, the insurance company for this insurance can pay the insured for the damage to his car and then later recover from the insurance company that provided the liability insurance for another car involved in the same accident. Such substantial recoveries normally do not occur for auto liability insurance for bodily injury and property damage.

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