Autonomous Vehicle Use and Urban Space Transformation: A Scenario Building and Analysing Method

The use of autonomous vehicles (AVs) has the potential to transform users’ behaviour and urban space management. Quantitative and qualitative analyses of impacts require a scenario building method. We considered the fleet size, modal share, car ownership, parking preferences, and urban space repurposing during the elaboration of a novel method. Existing scenarios and results of a questionnaire survey have been used as sources. The method was applied to build scenarios in a case study in Budapest, Hungary. The results were used to calculate the impacts on urban space management, including environmental savings. The key findings are: scenarios with significant shared AV use show that parking demand may be minimised (almost 83%) and urban space repurposing has the highest potential; furthermore, AV use and sharing acceptability may decrease the fleet size and alter the type of shared mode to multiple occupancies. The developed scenario building method serves as a base for future studies. The produced scenarios allow the researchers to focus on the analysis of the impacts caused.

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Autonomous vehicles and urban space management

Scientific Journal of Silesian University of Technology Series Transport ◽

10.20858/sjsutst.2021.110.14 ◽

2021 ◽

Vol 110 ◽

pp. 169-181

Author(s):

Dahlen SIQUEIRA SILVA ◽

Csaba CSISZÁR ◽

Dávid FÖLDES

Keyword(s):

Urban Space ◽

Autonomous Vehicles ◽

Estimation Method ◽

Real Data ◽

Vehicle Ownership ◽

Space Management ◽

Traffic Lane ◽

Bike Sharing ◽

Urban Parking ◽

Parking Demand

Discussions on how urban space would be transformed by the use of autonomous vehicles (AVs) are scarce. This study identifies the impacts caused by the shared use of AVs on urban parking and urban space management. An estimation method was formulated considering the reduction in parking demand, the possible alteration in vehicle ownership, and the reallocation of urban space. A case study was performed in a 673,220 m2 area through scenarios created by using real data of parking spaces and the results of previous studies. Results showed that parking spaces can be saved with the use of shared AVs, which would allow the reallocation of urban space to new uses (for example, implementation of around 12,000 bike-sharing docking spots, 10 km bike lanes, 7 km additional traffic lane or 140 ‘parklets’). The results contribute to revealing the positive impacts of AVs.

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Autonomous Electric Vehicle Sharing System Design

Journal of Mechanical Design ◽

10.1115/1.4034471 ◽

2016 ◽

Vol 139 (1) ◽

Cited By ~ 24

Author(s):

Namwoo Kang ◽

Fred M. Feinberg ◽

Panos Y. Papalambros

Keyword(s):

System Design ◽

Electric Vehicle ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Transportation Systems ◽

Fleet Size ◽

Gasoline Engines ◽

Ann Arbor ◽

Vehicle Technologies ◽

System Design Optimization

Car sharing services promise “green” transportation systems. Two vehicle technologies offer marketable, sustainable sharing: autonomous vehicles (AVs) eliminate customer requirements for car pick-up and return, and battery electric vehicles entail zero emissions. Designing an autonomous electric vehicle (AEV) fleet must account for the relationships among fleet operations, charging station (CS) operations, electric powertrain performance, and consumer demand. This paper presents a system design optimization framework integrating four subsystem problems: fleet size and assignment schedule; number and locations of charging stations; vehicle powertrain requirements; and service fees. We also compare an AEV service and autonomous vehicle (AV) service with gasoline engines. A case study for an autonomous fleet operating in Ann Arbor, MI, is used to examine AEV and AV sharing systems profitability and feasibility for a variety of market scenarios. The results provide practical insights for service system decision makers.

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Autonomous Vehicles within the Urban Space and Transport Security Challenges: Legal Aspect

Journal of Politics and Law ◽

10.5539/jpl.v13n3p133 ◽

2020 ◽

Vol 13 (3) ◽

pp. 133

Author(s):

Denis V. Iroshnikov ◽

Lyubov Yu. Larina ◽

Aleksandr I. Sidorkin

Keyword(s):

Urban Space ◽

Autonomous Vehicles ◽

Criminal Liability ◽

Autonomous Vehicle ◽

Legal Aspect ◽

Urban Traffic ◽

Autonomous Operation ◽

Road Users ◽

Operation Rules ◽

Vehicle Inspection

Nowadays autonomous vehicles are getting widespread use in different parts of the world. In some countries, they are being tested within the urban traffic whereas other counties have been already operating them. Such vehicles possess a number of obvious advantages. We cannot but agree that these cars are the future. However, before complete implementation and mass use of autonomous transport on public roads, it is necessary to resolve a number of problems concerning their safety towards road-users. Except for ethical, economic, and other aspects, it also embraces the legal aspect. The article analyses legal problems of ensuring transport security when using autonomous vehicles. It also touches upon the issues of obligations and liability. Special attention is paid to the matters of criminal liability for offences involving an autonomous vehicle. The conducted legal research allowed concluding that it is necessary to improve legislation in the sphere of operating such vehicles. It is essential to enshrine in law autonomous vehicles (whether fully-autonomous or partially-autonomous) operation rules, oblige their owners to perform regular diagnostic assessment, and to add demands to periodic vehicle inspection. When regulating criminal liability for harm caused by a self-driving vehicle, one must proceed from the layer of its autonomy which stipulates bringing the general public to responsibility.

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119855606 ◽

2019 ◽

Vol 2673 (12) ◽

pp. 494-507 ◽

Cited By ~ 1

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.

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

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211009084 ◽

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.

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

Sensors ◽

10.3390/s21062244 ◽

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.

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

Open Engineering ◽

10.1515/eng-2020-0016 ◽

2020 ◽

Vol 10 (1) ◽

pp. 175-182 ◽

Cited By ~ 1

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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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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Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads

Energies ◽

10.3390/en14123425 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3425

Author(s):

Huanping Li ◽

Jian Wang ◽

Guopeng Bai ◽

Xiaowei Hu

Keyword(s):

Traffic Flow ◽

Autonomous Vehicles ◽

Flow Model ◽

Road Traffic ◽

Autonomous Vehicle ◽

Transformation Model ◽

Hydrodynamic Theory ◽

Traffic Flow Model ◽

Traffic System ◽

The Stability

In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbitrary proportion (p) of autonomous vehicles. Using calculus and difference methods, a speed transformation model is established which could make the autonomous/human-driven vehicles maintain synchronized speed changes. Based on multi-hydrodynamic theory, a mixed traffic flow model capable of numerical calculation is established to predict the changes in traffic flow under different proportions of autonomous vehicles, then obtain the redistribution characteristics of traffic flow. Results show that the reaction time of autonomous vehicles has a decisive influence on traffic capacity; the q-k curve for mixed human/autonomous traffic remains in the region between the q-k curves for 100% human and 100% autonomous traffic; the participation of autonomous vehicles won’t bring essential changes to road traffic parameters; the speed-following transformation model minimizes the safety distance and provides a reference for the bottom program design of autonomous vehicles. In general, the research could not only optimize the stability of transportation system operation but also save road resources.

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Autonomous Navigation of a Team of Unmanned Surface Vehicles for Intercepting Intruders on a Region Boundary

Sensors ◽

10.3390/s21010297 ◽

2021 ◽

Vol 21 (1) ◽

pp. 297

Author(s):

Ali Marzoughi ◽

Andrey V. Savkin

Keyword(s):

Motion Control ◽

Autonomous Vehicles ◽

Autonomous Navigation ◽

Control Algorithm ◽

Autonomous Vehicle ◽

Sufficient Conditions ◽

Real Life ◽

Unmanned Surface Vehicles ◽

Necessary And Sufficient ◽

See Region

We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders.

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