Multi-level cooperation between the driver and an automated driving system during lane change maneuver

2016 ◽  
Author(s):  
M. A. Benloucif ◽  
J. C. Popieul ◽  
C. Sentouh
Keyword(s):  
Lane Change ◽  
Automated Driving ◽  
Driving System ◽  
Multi Level

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 Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2259 ◽  
Author(s):  
Chang Wang ◽  
Qinyu Sun ◽  
Zhen Li ◽  
Hongjia Zhang
Keyword(s):  
Risk Perception ◽  
Autonomous Vehicles ◽  
Decision Model ◽  
Signal Detection Theory ◽  
Critical Issue ◽  
Lane Change ◽  
Mixed Traffic ◽  
Driving System ◽  
Lane Changes ◽  
The Subject

Determining an appropriate time to execute a lane change is a critical issue for the development of Autonomous Vehicles (AVs).However, few studies have considered the rear and the front vehicle-driver’s risk perception while developing a human-like lane-change decision model. This paper aims to develop a lane-change decision model for AVs and to identify a two level threshold that conforms to a driver’s perception of the ability to safely change lanes with a rear vehicle approaching fast. Based on the signal detection theory and extreme moment trials on a real highway, two thresholds of safe lane change were determined with consideration of risk perception of the rear and the subject vehicle drivers, respectively. The rear vehicle’s Minimum Safe Deceleration (MSD) during the lane change maneuver of the subject vehicle was selected as the lane change safety indicator, and was calculated using the proposed human-like lane-change decision model. The results showed that, compared with the driver in the front extreme moment trial, the driver in the rear extreme moment trial is more conservative during the lane change process. To meet the safety expectations of the subject and rear vehicle drivers, the primary and secondary safe thresholds were determined to be 0.85 m/s2 and 1.76 m/s2, respectively. The decision model can help make AVs safer and more polite during lane changes, as it not only improves acceptance of the intelligent driving system, but also further ensures the rear vehicle’s driver’s safety.

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