Generalized Stable User Matching for Autonomous Vehicle Co-Ownership Programs

2020 ◽  
Vol 12 (2-3) ◽  
pp. 61-79 ◽  
Author(s):  
Anpeng Zhang ◽  
Jee Eun Kang ◽  
Changhyun Kwon
Keyword(s):  
Autonomous Vehicles ◽  
Ad Hoc ◽  
Autonomous Vehicle ◽  
Stable Matching ◽  
Computational Time ◽  
Combinatorial Properties ◽  
Fractional Ownership ◽  
Shared Mobility ◽  
New Form

We investigate a new form of car-sharing system that can be introduced in the market for autonomous vehicles called fractional ownership or co-ownership. Although dynamic ride sharing provides ad hoc shared mobility services without any long-term commitment, we consider co-ownership programs with which users can still “own” a car with committed usage and ownership. We assume that an autonomous vehicle is shared by a group of users, which is only accessible by the group. We use stable matching to help users find an appropriate group with which to share an autonomous vehicle and present a generalized stable matching model that allows flexible sizes of groups as well as various alternative objectives. We also present a heuristic algorithm to improve computational time owing to the combinatorial properties of the problem.

scholarly journals An Efficient Solving Method to Vehicle and Passenger Matching Problem for Sharing Autonomous Vehicle System

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ming Li ◽  
Nan Zheng ◽  
Xinkai Wu ◽  
Weihua Li ◽  
Jianhua Wu
Keyword(s):  
Autonomous Vehicles ◽  
Numerical Experiments ◽  
Time Windows ◽  
Autonomous Vehicle ◽  
Matching Problem ◽  
Large Size ◽  
Vehicle System ◽  
Shared Mobility ◽  
Minimum Number ◽  
Vehicle Capacity

With the potential of increasing mobility and reducing cost, shared mobility of autonomous vehicles (AVs) is going to gain solid growth in the coming decade. The major issue for the shared use of AVs is how to project serving routes in an efficiently way. From another perspective, this issue could be understood as to segment maximum number of passengers into groups. Therefore, this paper intends to investigate passengers’ similarity instead of directly matching AVs and passengers. The goal is to determine the minimum number of groups and assign each group with an AV. To this end, a cluster-based algorithm is proposed to classify passengers. Numerical experiments with both small-size and large-size demands are performed to present the validity of the proposed algorithm. Results indicate that the cluster-based algorithm could bring benefit to minimizing the number of vehicles and total travel distance. At last, sensitivity analysis of key parameters shows that vehicle capacity will have little impact when the number of seats exceeds four, and time windows could make continuous influence on gathering passengers.

scholarly journals Common Driving Notification Protocol Based on Classified Driving Behavior for Cooperation Intelligent Autonomous Vehicle Using Vehicular Ad-Hoc Network Technology

2015 ◽  
Vol 5 (1) ◽  
pp. 5-21 ◽  
Author(s):  
Che-Hung Lin ◽  
Fang-Yan Dong ◽  
Kaoru Hirota
Keyword(s):  
Autonomous Vehicles ◽  
Ad Hoc ◽  
Autonomous Vehicle ◽  
Driving Behavior ◽  
Packet Transmission ◽  
Wireless Data ◽  
Standard Format ◽  
Simulation Tools ◽  
Identification Time ◽  
Basic Protocol

Abstract A protocol, called common driving notification protocol (CDNP), is proposed based on the classified driving behavior for intelligent autonomous vehicles, and it defines a standard with common messages and format for vehicles. The common standard format and definitions of CDNP packet make the autonomous vehicles have a common language to exchange more detail driving decision information of various driving situations, decrease the identification time for one vehicle to identify the driving decisions of other vehicles before or after those driving decisions are performed. The simulation tools, including NS- 3 and SUMO, are used to simulate the wireless data packet transmission and the vehicle mobility; the experiment results present that the proposed protocol, CDNP, can increase the reaction preparing time with maximum value 250 seconds, decrease the identification time and the average travel time. Prospectively, it is decided to implement the CDNP as a protocol stack in the Linux kernel to provide the basic protocol capability for real world transmission testing.

scholarly journals Autonomous vehicles and smart cities: future directions of ownership vs shared mobility

2020 ◽  
Author(s):  
Nacer-Eddine Bezai ◽  
◽  
Benachir Medjdoub ◽  
Fodil Fadli ◽  
Moulay Larby Chalal ◽  
...  
Keyword(s):  
Public Transportation ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Travel Speed ◽  
On Demand ◽  
Shared Mobility ◽  
The Future ◽  
Self Driving Cars

Over the last decade, there has been increasing discussions about self-driving cars and how most auto-makers are racing to launch these products. However, this discourse is not limited to transportation only, but how such vehicles will affect other industries and specific aspects of our daily lives as future users such as the concept of work while being driven and productivity, entertainment, travel speed, and deliveries. Although these technologies are beneficial, access to these potentials depends on the behaviour of their users. There is a lack of a conceptual model that elucidate the acceptance of people to Self-driving cars. Service on-demand and shared mobility are the most critical factors that will ensure the successful adoption of these cars. This paper presents an analysis of public opinions in Nottingham, UK, through a questionnaire about the future of Autonomous vehicles' ownership and the extent to which they accept the idea of vehicle sharing. Besides, this paper tests two hypotheses. Firstly, (a) people who usually use Public transportation like (taxi, bus, tram, train, carpooling) are likely to share an Autonomous Vehicle in the future. Secondly, (b) people who use Private cars are expected to own an Autonomous Vehicle in the future. To achieve this aim, a combination of statistical methods such as logistic regression has been utilised. Unexpectedly, the study findings suggested that AVs ownership will increase contrary to what is expected, that Autonomous vehicles will reduce ownership. Besides, participants have shown low interest in sharing AVs. Therefore, it is likely that ownership of AVs will increase for several reasons as expressed by the participants such as safety, privacy, personal space, suitability to children and availability. Actions must be taken to promote shared mobility to avoid AVs possession growth. The ownership diminution, in turn, will reduce traffic congestion, energy and transport efficiency, better air quality. That is why analysing the factors that influence the mindset and attitude of people will enable us to understand how to shift from private cars to transport-on-demand, which is a priority rather than promoting the technology.

scholarly journals An Audification and Visualization System (AVS) of an Autonomous Vehicle for Blind and Deaf People Based on Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5035 ◽  
Author(s):  
Son ◽  
Jeong ◽  
Lee
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Sensor Data ◽  
Computational Time ◽  
Deaf People ◽  
Instrument Cluster ◽  
Text Data ◽  
Visualization System ◽  
Cloud Server

When blind and deaf people are passengers in fully autonomous vehicles, an intuitive and accurate visualization screen should be provided for the deaf, and an audification system with speech-to-text (STT) and text-to-speech (TTS) functions should be provided for the blind. However, these systems cannot know the fault self-diagnosis information and the instrument cluster information that indicates the current state of the vehicle when driving. This paper proposes an audification and visualization system (AVS) of an autonomous vehicle for blind and deaf people based on deep learning to solve this problem. The AVS consists of three modules. The data collection and management module (DCMM) stores and manages the data collected from the vehicle. The audification conversion module (ACM) has a speech-to-text submodule (STS) that recognizes a user’s speech and converts it to text data, and a text-to-wave submodule (TWS) that converts text data to voice. The data visualization module (DVM) visualizes the collected sensor data, fault self-diagnosis data, etc., and places the visualized data according to the size of the vehicle’s display. The experiment shows that the time taken to adjust visualization graphic components in on-board diagnostics (OBD) was approximately 2.5 times faster than the time taken in a cloud server. In addition, the overall computational time of the AVS system was approximately 2 ms faster than the existing instrument cluster. Therefore, because the AVS proposed in this paper can enable blind and deaf people to select only what they want to hear and see, it reduces the overload of transmission and greatly increases the safety of the vehicle. If the AVS is introduced in a real vehicle, it can prevent accidents for disabled and other passengers in advance.

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

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.

Defining Psychometric Variables Related to Use of Autonomous Vehicles

2019 ◽  
Vol 2673 (12) ◽  
pp. 655-669
Author(s):  
Yanbo Ge ◽  
Andisheh Ranjbari ◽  
Elyse O’C. Lewis ◽  
Eric Barber ◽  
Don MacKenzie
Keyword(s):  
Factor Analysis ◽  
Travel Behavior ◽  
Latent Variables ◽  
Autonomous Vehicles ◽  
Ad Hoc ◽  
Autonomous Vehicle ◽  
Behavioral Studies ◽  
Perceptions And Attitudes ◽  
Transportation Modes ◽  
Use Behavior

With the goal of understanding autonomous vehicle (AV) adoption and use behavior, numerous behavioral studies and surveys have included variables intended to capture individuals’ perceptions of and attitudes toward AVs. However, the selection of questions to measure these psychometric variables appears to be ad hoc and, in many cases, arbitrary. In contrast, this study defines psychometric latent variables (LVs) that are related to the adoption and use of AVs and develops a set of questions to reliably measure them. By considering three psychological concepts (norms, perceptions, and attitudes) and nine qualitative utility constructs that influence individuals’ travel behavior, this study defines a comprehensive list of LVs and identifier questions to support their construction. A factor analysis of a nationwide n = 347 sample was used to obtain a minimum set of relevant LVs and questions to measure them. Ultimately, the factor analysis resulted in a final set of nine LVs specified by 44 questions (four or five questions for each LV). The final set of questions may be used by researchers or survey organizations interested in studying future trends of demand and adoption for AVs or other emerging transportation modes. The approach used in this study may also be employed in other contexts to define psychometric variables of interest and the questions needed to reliably measure them.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

scholarly journals Modelling of the system “driver - automation - autonomous vehicle - road”

2020 ◽  
Vol 10 (1) ◽  
pp. 175-182 ◽  
Author(s):  
Grzegorz Koralewski
Keyword(s):  
Autonomous Vehicles ◽  
Combustion Engine ◽  
Autonomous Vehicle ◽  
Application Software ◽  
Motion Simulation ◽  
Gear Shift ◽  
Shift Control ◽  
Driving Torque ◽  
Physical Quantities ◽  
Motion Quality

AbstractThe work presents a simulation model of a “driver–automation–autonomous vehicles–road” system which is the basis for synthesis of automatic gear shift control system. The mathematical description makes use of physical quantities which characterise driving torque transformation from the combustion engine to the car driven wheels. The basic components of the model are algorithms for the driver’s action logic in controlling motion velocity, logic of gear shift control functioning regarding direction and moment of switching, for determining right-hand side of differential equations and for motion quality indicators. The model is realised in a form of an application software package, comprising sub-programmes for input data, for computerised motion simulation of cars with mechanical and hydro-mechanical – automatically controlled – transmission systems and for models of characteristic car routes.

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