Auction Based Parking Lot Assignment and Empty Cruising Limitation of Privately Owned Autonomous Vehicles in a Simple City Model

2021 ◽  
Author(s):  
Levente Alekszejenko ◽  
Tadeusz Dobrowiecki
Keyword(s):  
Autonomous Vehicles ◽  
Parking Lot ◽  
City Model ◽  
Privately Owned

scholarly journals Automated Parking Space Allocation during Transition with both Human-Operated and Autonomous Vehicles

2021 ◽  
Vol 11 (2) ◽  
pp. 855
Author(s):  
Mingkang Wu ◽  
Haobin Jiang ◽  
Chin-An Tan
Keyword(s):  
Autonomous Vehicles ◽  
Transition Period ◽  
Search Algorithm ◽  
Parking Space ◽  
Key Factors ◽  
Comprehensive Method ◽  
Space Allocation ◽  
Shortest Distance ◽  
Parking Lot ◽  
Ranking Order

As fully automated valet parking systems are being developed, there is a transition period during which both human-operated vehicles (HVs) and autonomous vehicles (AVs) are present in the same parking infrastructure. This paper addresses the problem of allocation of a parking space to an AV without conflicting with the parking space chosen by the driver of a HV. A comprehensive assessment of the key factors that affect the preference and choice of a driver for a parking space is established by the fuzzy comprehensive method. The algorithm then generates a ranking order of the available parking spaces to first predict the driver’s choice of parking space and then allocate a space for the AV. The Floyd algorithm of shortest distance is used to determine the route for the AV to reach its parking space. The proposed allocation and search algorithm is applied to the examples of a parking lot with three designed scenarios. It is shown that parking space can be reasonably allocated for AVs.

scholarly journals Crowdsourced Quantification and Visualization of Urban Mobility Space Inequality

2018 ◽  
Vol 3 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Michael Szell
Keyword(s):  
Urban Space ◽  
Autonomous Vehicles ◽  
Urban Mobility ◽  
Sustainable Transport ◽  
Sustainable Mobility ◽  
Policy Makers ◽  
The Public ◽  
Space Allocation ◽  
Crowdsourced Data ◽  
Privately Owned

Most cities are car-centric, allocating a privileged amount of urban space to cars at the expense of sustainable mobility like cycling. Simultaneously, privately owned vehicles are vastly underused, wasting valuable opportunities for accommodating more people in a livable urban environment by occupying spacious parking areas. Since a data-driven quantification and visualization of such urban mobility space inequality is lacking, here we explore how crowdsourced data can help to advance its understanding. In particular, we describe how the open-source online platform What the Street!? uses massive user-generated data from OpenStreetMap for the interactive exploration of city-wide mobility spaces. Using polygon packing and graph algorithms, the platform rearranges all parking and mobility spaces of cars, rails, and bicycles of a city to be directly comparable, making mobility space inequality accessible to a broad public. This crowdsourced method confirms a prevalent imbalance between modal share and space allocation in 23 cities worldwide, typically discriminating bicycles. Analyzing the guesses of the platform’s visitors about mobility space distributions, we find that this discrimination is consistently underestimated in the public opinion. Finally, we discuss a visualized scenario in which extensive parking areas are regained through fleets of shared, autonomous vehicles. We outline how such accessible visualization platforms can facilitate urban planners and policy makers to reclaim road and parking space for pushing forward sustainable transport solutions.

scholarly journals Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 161 ◽  
Author(s):  
Junqiao Zhao ◽  
Yewei Huang ◽  
Xudong He ◽  
Shaoming Zhang ◽  
Chen Ye ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Indoor Localization ◽  
Autonomous Driving ◽  
Human Beings ◽  
Low Level ◽  
Localization And Mapping ◽  
Challenging Tasks ◽  
Parking Lot ◽  
Feature Based

Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.

scholarly journals Conjoint Vehicle License Plate Identification System

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.

Multi-collaborative Regression Convolutional Neural Network for Vehicle Angle Detection

2020 ◽  
Vol 13 (8) ◽  
pp. 1227-1241
Author(s):  
Guoqiang Chen ◽  
Mengchao Liu ◽  
Hongpeng Zhou ◽  
Bingxin Bai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Training Sample ◽  
Detection Accuracy ◽  
Detection Error ◽  
Detection Failure ◽  
Pose Detection ◽  
Parking Lot

Background: The vehicle pose detection plays an important role in monitoring vehicle behavior and the parking situation. The real-time detection of vehicle pose with high accuracy is of great importance. Objective: The goal of the work is to construct a new network to detect the vehicle angle based on the regression Convolutional Neural Network (CNN). The main contribution is that several traditional regression CNNs are combined as the Multi-Collaborative Regression CNN (MCR-CNN), which greatly enhances the vehicle angle detection precision and eliminates the abnormal detection error. Methods: Two challenges with respect to the traditional regression CNN have been revealed in detecting the vehicle pose angle. The first challenge is the detection failure resulting from the conversion of the periodic angle to the linear angle, while the second is the big detection error if the training sample value is very small. An MCR-CNN is proposed to solve the first challenge. And a 2- stage method is proposed to solve the second challenge. The architecture of the MCR-CNN is designed in detail. After the training and testing data sets are constructed, the MCR-CNN is trained and tested for vehicle angle detection. Results: The experimental results show that the testing samples with the error below 4° account for 95% of the total testing samples based on the proposed MCR-CNN. The MCR-CNN has significant advantages over the traditional vehicle pose detection method. Conclusion: The proposed MCR-CNN cannot only detect the vehicle angle in real-time, but also has a very high detection accuracy and robustness. The proposed approach can be used for autonomous vehicles and monitoring of the parking lot.

scholarly journals Crowdsourced Quantification and Visualization of Urban Mobility Space Inequality

2018 ◽  
Author(s):  
Michael Szell
Keyword(s):  
Urban Space ◽  
Autonomous Vehicles ◽  
Urban Mobility ◽  
Sustainable Transport ◽  
Sustainable Mobility ◽  
Policy Makers ◽  
The Public ◽  
Space Allocation ◽  
Crowdsourced Data ◽  
Privately Owned

Most cities are car-centric, allocating a privileged amount of urban space to cars at the expense of sustainable mobility like cycling. Simultaneously, privately owned vehicles are vastly underused, wasting valuable opportunities for accommodating more people in a livable urban environment by occupying spacious parking areas. Since a data-driven quantification and visualization of such urban mobility space inequality is lacking, here we explore how crowdsourced data can help to advance its understanding. In particular, we describe how the open-source online platform What the Street!? uses massive user-generated data from OpenStreetMap for the interactive exploration of city-wide mobility spaces. Using polygon packing and graph algorithms, the platform rearranges all parking and mobility spaces of cars, rails, and bicycles of a city to be directly comparable, making mobility space inequality accessible to a broad public. This crowdsourced method confirms a prevalent imbalance between modal share and space allocation in 23 cities worldwide, typically discriminating bicycles. Analyzing the guesses of the platform’s visitors about mobility space distributions, we find that this discrimination is consistently underestimated in the public opinion. Finally, we discuss a visualized scenario in which extensive parking areas are regained through fleets of shared, autonomous vehicles. We outline how such accessible visualization platforms can facilitate urban planners and policy makers to reclaim road and parking space for pushing forward sustainable transport solutions.

scholarly journals What to do in the Meantime: A Service Coverage Analysis for Parked Autonomous Vehicles

2020 ◽  
Vol 1 ◽  
pp. 1-15
Author(s):  
Steffen Illium ◽  
Philipp Andreas Friese ◽  
Robert Müller ◽  
Sebastian Feld
Keyword(s):  
Autonomous Vehicles ◽  
Transition Period ◽  
Working Hours ◽  
Coverage Analysis ◽  
Telematics Service ◽  
Service Coverage ◽  
Business Week ◽  
Available Technology ◽  
The Impact ◽  
Privately Owned

Abstract. Fully autonomously driving vehicles are expected to be a widely available technology in the near future. Privately owned cars, which remain parked for the majority of their lifetime, may therefore be capable of driving independently during their usual long parking periods (e.g. their owners working hours). Our analysis aims to focus on the potential of a privately owned shared car concept as transition period between the present usages of privately owned cars towards a transportation paradigm of privately owned shared autonomous vehicles. We propose two methods in the field of reachability analysis to evaluate the impact of such vehicles during parking periods. Our proposed methods are applied to a dataset of parking times of users of a telematics service provider in the Munich area (Germany). We show the impact of time and location dependent effects on the analyzed service coverage, such as business week rush hours or cover age divergence between urban and suburban regions.

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