Use of autonomous vehicles for drivers with various movement disputions

2020 ◽  
Vol 129 ◽  
pp. 17-26
Author(s):  
Beata Stasiak-Cieślak ◽  
Piotr Malawko ◽  
Anna Skarbek-Żabkin ◽  
Tomasz Szczepański
Keyword(s):  
Autonomous Vehicles ◽  
Physical Disabilities ◽  
People With Disabilities ◽  
Autonomous Driving ◽  
Current Level ◽  
Basic Solution ◽  
Continuous Development ◽  
Motor Transport ◽  
2Nd Generation ◽  
Autonomous Cars

To the continuous development of technology, more and more opportunities are opened in issues related to the mobility of people with disabilities. One of the problems among these drivers is the relatively fast fatigue caused by driving. The basic solution is of course the optimization of adaptive devices in terms of ergonomics. At the same time, a new technological trend is emerging in the form of autonomous cars. There is therefore the possibility of partially relieving drivers with physical disabilities. People susceptible to fast fatigue at the wheel, they will be able to rest periodically, thanks to the support of driving the car by autonomous driving. This article focuses on combining the types of motor dysfunctions with the available autonomous driving capabilities. Applications are based on tests carried out at the Motor Transport Institute in cooperation with Tesla Warszawa. The autonomous functions will be described on the basis of the possibilities offered on Tesla cars, with the autopilot of the 2nd generation, with software version 9.0. It seems that at the current level one can talk about significant help in driving a car that improves the mobility conditions of people with motor dysfunctions. In the light of this observation, also the estimation regarding the usefulness of future solutions seems to be unequivocally decided to support the legitimacy of the development of autonomous driving technology.

scholarly journals Ethical Challenges and Countermeasures of Autonomous Vehicles

2021 ◽  
Vol 233 ◽  
pp. 04016
Author(s):  
Xi Zhu ◽  
Zhiqiang Gu ◽  
Zhen Wang
Keyword(s):  
21St Century ◽  
Autonomous Vehicles ◽  
New Technologies ◽  
Ethical Dilemmas ◽  
Autonomous Driving ◽  
Intelligent Vehicle ◽  
Connected Vehicles ◽  
Ethical Challenges ◽  
Autonomous Cars ◽  
Vehicle Market

Autonomous driving is one of the greatest new technologies in the 21st century, but to a certain extent, it has caused conflicts in bioethical standards and the ownership of accident responsibility. For the ethical dilemma of autonomous cars, every countrie currently does not have a perfect solution. First of all, this paper predicts the development of intelligent connected vehicles through the analysis of the domestic and foreign intelligent vehicle market. Then lists and analyzes the legal and countermeasures of ethical dilemmas in various countries. According to the knowledge of psychology and ethics, this paper puts forward a way to solve this problem which is more in line with the development of industry and ethics.

Autonomy and Responsibility in Hybrid Systems

2017 ◽  
Author(s):  
Wulf Loh ◽  
Janina Loh
Keyword(s):  
Hybrid Systems ◽  
Hybrid System ◽  
Moral Dilemma ◽  
Autonomous Driving ◽  
Shared Responsibility ◽  
Driving System ◽  
Autonomous Cars ◽  
Self Driving Cars ◽  
Trolley Cases ◽  
Autonomous Driving System

In this chapter, we give a brief overview of the traditional notion of responsibility and introduce a concept of distributed responsibility within a responsibility network of engineers, driver, and autonomous driving system. In order to evaluate this concept, we explore the notion of man–machine hybrid systems with regard to self-driving cars and conclude that the unit comprising the car and the operator/driver consists of such a hybrid system that can assume a shared responsibility different from the responsibility of other actors in the responsibility network. Discussing certain moral dilemma situations that are structured much like trolley cases, we deduce that as long as there is something like a driver in autonomous cars as part of the hybrid system, she will have to bear the responsibility for making the morally relevant decisions that are not covered by traffic rules.

Female Voice Agents in Fully Autonomous Vehicles Are Not Only More Likeable and Comfortable, But Also More Competent

2020 ◽  
Vol 64 (1) ◽  
pp. 1033-1037
Author(s):  
Jiayuan Dong ◽  
Emily Lawson ◽  
Jack Olsen ◽  
Myounghoon Jeon
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Preference Ranking ◽  
Uncanny Valley ◽  
Tracking Data ◽  
Effective Solution ◽  
User Perception ◽  
Future Studies ◽  
Underlying Mechanisms ◽  
The Voice

Driving agents can provide an effective solution to improve drivers’ trust in and to manage interactions with autonomous vehicles. Research has focused on voice-agents, while few have explored robot-agents or the comparison between the two. The present study tested two variables - voice gender and agent embodiment, using conversational scripts. Twenty participants experienced autonomous driving using the simulator for four agent conditions and filled out subjective questionnaires for their perception of each agent. Results showed that the participants perceived the voice only female agent as more likeable, more comfortable, and more competent than other conditions. Their final preference ranking also favored this agent over the others. Interestingly, eye-tracking data showed that embodied agents did not add more visual distractions than the voice only agents. The results are discussed with the traditional gender stereotype, uncanny valley, and participants’ gender. This study can contribute to the design of in-vehicle agents in the autonomous vehicles and future studies are planned to further identify the underlying mechanisms of user perception on different agents.

scholarly journals Modules and Techniques for Motion Planning: An Industrial Perspective

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 420
Author(s):  
Stefano Quer ◽  
Luz Garcia
Keyword(s):  
Data Fusion ◽  
Autonomous Driving ◽  
Random Trees ◽  
Approximation Techniques ◽  
Main Research ◽  
Autonomous Cars ◽  
Critical Issues ◽  
Relative Errors ◽  
Path Planner ◽  
Many Core

Research on autonomous cars has become one of the main research paths in the automotive industry, with many critical issues that remain to be explored while considering the overall methodology and its practical applicability. In this paper, we present an industrial experience in which we build a complete autonomous driving system, from the sensor units to the car control equipment, and we describe its adoption and testing phase on the field. We report how we organize data fusion and map manipulation to represent the required reality. We focus on the communication and synchronization issues between the data-fusion device and the path-planner, between the CPU and the GPU units, and among different CUDA kernels implementing the core local planner module. In these frameworks, we propose simple representation strategies and approximation techniques which guarantee almost no penalty in terms of accuracy and large savings in terms of memory occupation and memory transfer times. We show how we adopt a recent implementation on parallel many-core devices, such as CUDA-based GPGPU, to reduce the computational burden of rapidly exploring random trees to explore the state space along with a given reference path. We report on our use of the controller and the vehicle simulator. We run experiments on several real scenarios, and we report the paths generated with the different settings, with their relative errors and computation times. We prove that our approach can generate reasonable paths on a multitude of standard maneuvers in real time.

scholarly journals Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3783
Author(s):  
Sumbal Malik ◽  
Manzoor Ahmed Khan ◽  
Hesham El-Sayed
Keyword(s):  
Autonomous Vehicles ◽  
Communication Technologies ◽  
Autonomous Driving ◽  
Use Cases ◽  
Cooperative Driving ◽  
Vehicle To Vehicle ◽  
Future Challenges ◽  
Vehicle To Infrastructure ◽  
High Level ◽  
Intersection Management

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1372-1376
Author(s):  
Gaojian Huang ◽  
Christine Petersen ◽  
Brandon J. Pitts
Keyword(s):  
Systematic Review ◽  
Autonomous Vehicles ◽  
Response Times ◽  
Autonomous Driving ◽  
Mental States ◽  
Manual Control ◽  
Monitoring Systems ◽  
Wide Range ◽  
Different Types ◽  
The Impact

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

scholarly journals On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data

2020 ◽  
Vol 13 (1) ◽  
pp. 89
Author(s):  
Manuel Carranza-García ◽  
Jesús Torres-Mateo ◽  
Pedro Lara-Benítez ◽  
Jorge García-Gutiérrez
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Remote Sensing Data ◽  
Autonomous Driving ◽  
Two Stage ◽  
Detection Systems ◽  
One Stage ◽  
Time Requirements ◽  
Speed Accuracy

Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the performance of existing 2D detection systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3) and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context. For the experimental study, we use the Waymo Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage detectors, our findings show that two-stage detectors still provide the most robust performance. Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage.

Autonomous vehicles: a review of the ethical, social and economic implications of the AI revolution

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shweta Banerjee
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Design Methodology ◽  
Technological Advancement ◽  
Economic Implications ◽  
Content Type ◽  
The Public ◽  
Autonomous Cars ◽  
The Subject ◽  
Practical Implications

PurposeThere are ethical, legal, social and economic arguments surrounding the subject of autonomous vehicles. This paper aims to discuss some of the arguments to communicate one of the current issues in the rising field of artificial intelligence.Design/methodology/approachMaking use of widely available literature that the author has read and summarised showcasing her viewpoints, the author shows that technology is progressing every day. Artificial intelligence and machine learning are at the forefront of technological advancement today. The manufacture and innovation of new machines have revolutionised our lives and resulted in a world where we are becoming increasingly dependent on artificial intelligence.FindingsTechnology might appear to be getting out of hand, but it can be effectively used to transform lives and convenience.Research limitations/implicationsFrom robotics to autonomous vehicles, countless technologies have and will continue to make the lives of individuals much easier. But, with these advancements also comes something called “future shock”.Practical implicationsFuture shock is the state of being unable to keep up with rapid social or technological change. As a result, the topic of artificial intelligence, and thus autonomous cars, is highly debated.Social implicationsThe study will be of interest to researchers, academics and the public in general. It will encourage further thinking.Originality/valueThis is an original piece of writing informed by reading several current pieces. The study has not been submitted elsewhere.

Decision making at unsignalized inner city intersections using discrete events systems

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Hannes Weinreuter ◽  
Balázs Szigeti ◽  
Nadine-Rebecca Strelau ◽  
Barbara Deml ◽  
Michael Heizmann
Keyword(s):  
Inner City ◽  
Road Safety ◽  
Autonomous Vehicles ◽  
Discrete Event ◽  
Autonomous Driving ◽  
Simulation Environment ◽  
Discrete Events ◽  
Real World Data ◽  
Event System ◽  
Discrete Events Systems

Abstract Autonomous driving is a promising technology to, among many aspects, improve road safety. There are however several scenarios that are challenging for autonomous vehicles. One of these are unsignalized junctions. There exist scenarios in which there is no clear regulation as to is allowed to drive first. Instead, communication and cooperation are necessary to solve such scenarios. This is especially challenging when interacting with human drivers. In this work we focus on unsignalized T-intersections. For that scenario we propose a discrete event system (DES) that is able to solve the cooperation with human drivers at a T-intersection with limited visibility and no direct communication. The algorithm is validated in a simulation environment, and the parameters for the algorithm are based on an analysis of typical human behavior at intersections using real-world data.

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