A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector

This work presents a novel platform for autonomous vehicle technologies research for the insurance sector. The platform has been collaboratively developed by the insurance company MAPFRE-CESVIMAP, Universidad Carlos III de Madrid and INSIA of the Universidad Politécnica de Madrid. The high-level architecture and several autonomous vehicle technologies developed using the framework of this collaboration are introduced and described in this work. Computer vision technologies for environment perception, V2X communication capabilities, enhanced localization, human–machine interaction and self awareness are among the technologies which have been developed and tested. Some use cases that validate the technologies presented in the platform are also presented; these use cases include public demonstrations, tests of the technologies and international competitions for self-driving technologies.

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An Open-Source Scale Model Platform for Teaching Autonomous Vehicle Technologies

Sensors ◽

10.3390/s21113850 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3850

Author(s):

Bastien Vincke ◽

Sergio Rodriguez Rodriguez Florez ◽

Pascal Aubert

Keyword(s):

Open Source ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Constrained Systems ◽

Scale Model ◽

Technical Students ◽

Vehicle Technologies ◽

Model Platform ◽

High Level ◽

Time And Energy

Emerging technologies in the context of Autonomous Vehicles (AV) have drastically evolved the industry’s qualification requirements. AVs incorporate complex perception and control systems. Teaching the associated skills that are necessary for the analysis of such systems becomes a very difficult process and existing solutions do not facilitate learning. In this study, our efforts are devoted to proposingan open-source scale model vehicle platform that is designed for teaching the fundamental concepts of autonomous vehicles technologies that are adapted to undergraduate and technical students. The proposed platform is as realistic as possible in order to present and address all of the fundamental concepts that are associated with AV. It includes all on-board components of a stand-alone system, including low and high level functions. Such functionalities are detailed and a proof of concept prototype is presented. A set of experiments is carried out, and the results obtained using this prototype validate the usability of the model for the analysis of time- and energy-constrained systems, as well as distributed embedded perception systems.

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

Sensors ◽

10.3390/s21113783 ◽

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.

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A Connected Autonomous Vehicle Testbed: Capabilities, Experimental Processes and Lessons Learned

Automation ◽

10.3390/automation1010002 ◽

2020 ◽

Vol 1 (1) ◽

pp. 17-32

Author(s):

Thomas Kent ◽

Anthony Pipe ◽

Arthur Richards ◽

Jim Hutchinson ◽

Wolfgang Schuster

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Lessons Learned ◽

Urban Setting ◽

Sensor Data ◽

Stable Set ◽

Research And Innovation ◽

Legal Frameworks ◽

High Level ◽

Key Steps

VENTURER was one of the first three UK government funded research and innovation projects on Connected Autonomous Vehicles (CAVs) and was conducted predominantly in the South West region of the country. A series of increasingly complex scenarios conducted in an urban setting were used to: (i) evaluate the technology created as a part of the project; (ii) systematically assess participant responses to CAVs and; (iii) inform the development of potential insurance models and legal frameworks. Developing this understanding contributed key steps towards facilitating the deployment of CAVs on UK roads. This paper aims to describe the VENTURER Project trials, their objectives and detail some of the key technologies used. Importantly we aim to introduce some informative challenges that were overcame and the subsequent project and technological lessons learned in a hope to help others plan and execute future CAV research. The project successfully integrated several technologies crucial to CAV development. These included, a Decision Making System using behaviour trees to make high level decisions; A pilot-control system to smoothly and comfortably turn plans into throttle and steering actuation; Sensing and perception systems to make sense of raw sensor data; Inter-CAV Wireless communication capable of demonstrating vehicle-to-vehicle communication of potential hazards. The closely coupled technology integration, testing and participant-focused trial schedule led to a greatly improved understanding of the engineering and societal barriers that CAV development faces. From a behavioural standpoint the importance of reliability and repeatability far outweighs a need for novel trajectories, while the sensor-to-perception capabilities are critical, the process of verification and validation is extremely time consuming. Additionally, the added capabilities that can be leveraged from inter-CAV communications shows the potential for improved road safety that could result. Importantly, to effectively conduct human factors experiments in the CAV sector under consistent and repeatable conditions, one needs to define a scripted and stable set of scenarios that uses reliable equipment and a controllable environmental setting. This requirement can often be at odds with making significant technology developments, and if both are part of a project’s goals then they may need to be separated from each other.

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An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications

Wireless Communications and Mobile Computing ◽

10.1155/2021/6621451 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Shicai Ji ◽

Ying Peng ◽

Hongjia Zhang ◽

Shengbo Wu

Keyword(s):

Autonomous Vehicles ◽

Recognition Accuracy ◽

Autonomous Vehicle ◽

Principal Component ◽

Semisupervised Learning ◽

Support Vector ◽

Human Machine Interaction ◽

Intention Recognition ◽

Comparison Results ◽

Pedestrian Crossing

One of the major challenges that connected autonomous vehicles (CAVs) are facing today is driving in urban environments. To achieve this goal, CAVs need to have the ability to understand the crossing intention of pedestrians. However, for autonomous vehicles, it is quite challenging to understand pedestrians’ crossing intentions. Because the pedestrian is a very complex individual, their intention to cross the street is affected by the weather, the surrounding traffic environment, and even his own emotions. If the established street crossing intention recognition model cannot be updated in real time according to the diversity of samples, the efficiency of human-machine interaction and the interaction safety will be greatly affected. Based on the above problems, this paper established a pedestrian crossing intention model based on the online semisupervised support vector machine algorithm (OS3VM). In order to verify the effectiveness of the model, this paper collects a large amount of pedestrian crossing data and vehicle movement data based on laser scanner, and determines the main feature components of the model input through feature extraction and principal component analysis (PCA). The comparison results of recognition accuracy of SVM, S3VM, and OS3VM indicate that the proposed OS3VM model exhibits a better ability to recognize pedestrian crossing intentions than the SVM and S3VM models, and the accuracy achieves 94.83%. Therefore, the OS3VM model can reduce the number of labeled samples for training the classifier and improve the recognition accuracy.

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Platoon Signal Priority in Connected-Autonomous Vehicle Environments: Algorithm Development and Testing

Put i saobraćaj ◽

10.31075/pis.65.04.01 ◽

2019 ◽

Vol 65 (4) ◽

pp. 1-9

Author(s):

Milan Zlatkovic ◽

Andalib Shams

Keyword(s):

Traffic Congestion ◽

Autonomous Vehicles ◽

Penetration Rate ◽

Autonomous Vehicle ◽

Transportation Systems ◽

Vehicle Technologies ◽

Measures Of Effectiveness ◽

Urban Corridor ◽

Intersection Control ◽

Day By Day

As traffic congestion increases day by day, it becomes necessary to improve the existing roadway facilities to maintain satisfactory operational and safety performances. New vehicle technologies, such as Connected and Autonomous Vehicles (CAV) have a potential to significantly improve transportation systems. Using the advantages of CAVs, this study developed signalized intersection control strategy algorithm that optimizes the operations of CAVs and allows signal priority for connected platoons. The algorithm was tested in VISSIM microsimulation using a real-world urban corridor. The tested scenarios include a 2040 Do-Nothing scenario, and CAV alternatives with 25%, 50%, 75% and 100% CAV penetration rate. The results show a significant reduction in intersection delays (26% - 38%) and travel times (6% - 20%), depending on the penetration rate, as well as significant improvements on the network-wide level. CAV penetration rates of 50% or more have a potential to significantly improve all operational measures of effectiveness.

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Conjoint Vehicle License Plate Identification System

The Open Transportation Journal ◽

10.2174/1874447802014010164 ◽

2020 ◽

Vol 14 (1) ◽

pp. 164-173

Author(s):

Yair Wiseman

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Automatic Identification ◽

Identification System ◽

License Plate ◽

Vehicle Identification ◽

Parking Lot ◽

High Level ◽

Identification Techniques ◽

The City

Background: An autonomous vehicle will go unaccompanied to park itself in a remote parking lot without a driver or a passenger inside. Unlike traditional vehicles, an autonomous vehicle can drop passengers off near any location. Afterward, instead of cruising for a nearby free parking, the vehicle can be automatically parked in a remote parking lot which can be in a rural fringe of the city where inexpensive land is more readily available. Objective: The study aimed at avoidance of mistakes in the identification of the vehicle with the help of the automatic identification device. Methods: It is proposed to back up license plate identification procedure by making use of three distinct identification techniques: RFID, Bluetooth and OCR with the aim of considerably reducing identification mistakes. Results: The RFID is the most reliable identification device but the Bluetooth and the OCR can improve the reliability of RFID. Conclusion: A very high level of reliable vehicle identification device is achievable. Parking lots for autonomous vehicles can be very efficient and low-priced. The critical difficulty is to automatically make sure that the autonomous vehicle is correctly identified at the gate.

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NEW SOURCE OF GEOSPATIAL DATA: CROWDSENSING BY ASSISTED AND AUTONOMOUS VEHICLE TECHNOLOGIES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w8-211-2018 ◽

2018 ◽

Vol XLII-4/W8 ◽

pp. 211-216 ◽

Cited By ~ 1

Author(s):

C. K. Toth ◽

Z. Koppanyi ◽

M. G. Lenzano

Keyword(s):

Real Time ◽

Autonomous Vehicles ◽

Data Science ◽

Information Dissemination ◽

Autonomous Vehicle ◽

Weather Conditions ◽

Traffic Monitoring ◽

Geospatial Data ◽

Object Space ◽

Vehicle Technologies

<p><strong>Abstract.</strong> The ongoing proliferation of remote sensing technologies in the consumer market has been rapidly reshaping the geospatial data acquisition world, and subsequently, the data processing as well as information dissemination processes. Smartphones have clearly established themselves as the primary crowdsourced data generators recently, and provide an incredible volume of remote sensed data with fairly good georeferencing. Besides the potential to map the environment of the smartphone users, they provide information to monitor the dynamic content of the object space. For example, real-time traffic monitoring is one of the most known and widely used real-time crowdsensed application, where the smartphones in vehicles jointly contribute to an unprecedentedly accurate traffic flow estimation. Now we are witnessing another milestone to happen, as driverless vehicle technologies will become another major source of crowdsensed data. Due to safety concerns, the requirements for sensing are higher, as the vehicles should sense other vehicles and the road infrastructure under any condition, not just daylight in favorable weather conditions, and at very fast speed. Furthermore, the sensing is based on using redundant and complementary sensor streams to achieve a robust object space reconstruction, needed to avoid collisions and maintain normal travel patterns. At this point, the remote sensed data in assisted and autonomous vehicles are discarded, or partially recorded for R&amp;D purposes. However, in the long run, as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies mature, recording data will become a common place, and will provide an excellent source of geospatial information for road mapping, traffic monitoring, etc. This paper reviews the key characteristics of crowdsourced vehicle data based on experimental data, and then the processing aspects, including the Data Science and Deep Learning components.</p>

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Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2665-01 ◽

2017 ◽

Vol 2665 (1) ◽

pp. 1-10 ◽

Cited By ~ 78

Author(s):

Patrícia S. Lavieri ◽

Venu M. Garikapati ◽

Chandra R. Bhat ◽

Ram M. Pendyala ◽

Sebastian Astroza ◽

...

Keyword(s):

Travel Behavior ◽

Autonomous Vehicles ◽

Network Performance ◽

Transportation Network ◽

Autonomous Vehicle ◽

Puget Sound ◽

Heterogeneous Data ◽

Model System ◽

Vehicle Technologies ◽

The One

Considerable interest exists in modeling and forecasting the effects of autonomous vehicles on travel behavior and transportation network performance. In an autonomous vehicle (AV) future, individuals may privately own such vehicles, use mobility-on-demand services provided by transportation network companies that operate shared AV fleets, or adopt a combination of those two options. This paper presents a comprehensive model system of AV adoption and use. A generalized, heterogeneous data model system was estimated with data collected as part of the Puget Sound, Washington, Regional Travel Study. The results showed that lifestyle factors play an important role in shaping AV usage. Younger, urban residents who are more educated and technologically savvy are more likely to be early adopters of AV technologies than are older, suburban and rural individuals, a fact that favors a sharing-based service model over private ownership. Models such as the one presented in this paper can be used to predict the adoption of AV technologies, and such predictions will, in turn, help forecast the effects of AVs under alternative future scenarios.

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From driver assistance to fully-autonomous: examining consumer acceptance of autonomous vehicle technologies

Journal of Consumer Marketing ◽

10.1108/jcm-10-2019-3441 ◽

2020 ◽

Vol 37 (7) ◽

pp. 883-894

Author(s):

Michael A. Erskine ◽

Stoney Brooks ◽

Timothy H. Greer ◽

Charles Apigian

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Consumer Acceptance ◽

Unified Theory ◽

Driver Assistance ◽

Content Type ◽

Use Of Technology ◽

Effort Expectancy ◽

Vehicle Technology ◽

Vehicle Technologies

Purpose The purpose of this paper is to inform researchers who are examining the adoption of autonomous vehicle technology and to provide marketing insights for developers and manufacturers of such vehicles and their ancillary technologies. Design/methodology/approach This study assesses consumer attitudes and behavioral intentions regarding autonomous vehicles (AV) by applying the consumer version of the unified theory of acceptance and use of technology (UTAUT2). We validate the model through a behavioral research study (n = 1,154). Findings The findings suggest that attitude toward AV is primarily formed through performance expectancy, effort expectancy, social influence and hedonic motivation. Furthermore, the level of autonomy has limited effects on attitude. Originality/value This is the first study to examine attitudes toward AV through the theoretical lens of UTAUT2. Additionally, this study provides insights into consumer perceptions and the corresponding effects on attitude by moderating the level of autonomy.

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An Overview of the Latest Progress and Core Challenge of Autonomous Vehicle Technologies

MATEC Web of Conferences ◽

10.1051/matecconf/202030806002 ◽

2020 ◽

Vol 308 ◽

pp. 06002

Author(s):

Zongwei Liu ◽

Hao Jiang ◽

Hong Tan ◽

Fuquan Zhao

Keyword(s):

Deep Learning ◽

Mass Production ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Autonomous Driving ◽

Future Trend ◽

Rapid Progress ◽

Vehicle Technologies

The mass production of autonomous vehicle is coming, thanks to the rapid progress of autonomous driving technology, especially the recent breakthroughs in LiDAR sensors, GPUs, and deep learning. Many automotive and IT companies represented by Waymo and GM are constantly promoting their advanced autonomous vehicles to hit public roads as early as possible. This paper systematically reviews the latest development and future trend of the autonomous vehicle technologies, discusses the extensive application of AI in ICV, and identifies the key problems and core challenges facing the commercialization of autonomous vehicle. Based on the review, it forecasts the prospects and conditions of autonomous vehicle’s mass production and points out the arduous, long-term and systematic nature of its development.

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