Quantifying the Potential Impact of Autonomous Vehicle Adoption on Government Finances

2019 ◽  
Vol 2673 (5) ◽  
pp. 72-83 ◽  
Author(s):  
Jacob Terry ◽  
Chris Bachmann
Keyword(s):  
Autonomous Vehicles ◽  
Public Discourse ◽  
Autonomous Vehicle ◽  
Primary Objective ◽  
Transportation Industry ◽  
Gas Tax ◽  
Vehicle To Infrastructure ◽  
Potential Impact ◽  
Canadian Provinces ◽  
The Cost

There is some understanding that autonomous vehicles will disrupt public sector policies and the existing transportation industry, but this disruption is often loosely defined and tends to ignore how it would affect governments financially. The primary objective of this paper is to quantify the short-term impact of introducing autonomous vehicles on government finances. The analysis focuses on eight Canadian governments, encompassing four government tiers. Public discourse and academic literature are used to generate nine predicted changes (forecast variables) in future adoption scenarios. Using the predicted rate of autonomous vehicle adoption, the remaining variables are converted into financial changes by combining them with government financial records, infrastructure inventory datasets, and project cost estimates. The results suggest that, while revenue impacts are fairly minimal, and mostly impact Canadian provinces, the cost of implementing the expected vehicle-to-infrastructure (V2I) communication upgrades could be expensive for governments with smaller populations, especially municipalities. The revenue analysis indicates the biggest shift is likely to be a loss in gas tax, which affects federal and provincial revenues, yet this share is relatively small compared with the size of these governments’ budgets. The expense analysis suggests that, although provinces have extensive road networks, the cost of upgrading all of their highways may not be unreasonable compared with their yearly revenue intake. On the other hand, municipalities would require substantial new funds to be able to make the same upgrades.

Witness of Things

2019 ◽  
Vol 7 (2) ◽  
pp. 72-87 ◽  
Author(s):  
Serkan Ayvaz ◽  
Salih Cemil Cetin
Keyword(s):  
Autonomous Vehicles ◽  
Vehicular Networks ◽  
Autonomous Vehicle ◽  
Content Type ◽  
Record Keeping ◽  
Vehicle To Vehicle ◽  
Autonomous Cars ◽  
Vehicle To Infrastructure ◽  
Governmental Institutions ◽  
Self Driving Cars

Purpose The purpose of this paper is to develop a model for autonomous cars to establish trusted parties by combining distributed ledgers and self-driving cars in the traffic to provide single version of the truth and thus build public trust. Design/methodology/approach The model, which the authors call Witness of Things, is based on keeping decision logs of autonomous vehicles in distributed ledgers through the use of vehicular networks and vehicle-to-vehicle/vehicle-to-infrastructure (or vice versa) communications. The model provides a single version of the truth and thus helps enable the autonomous vehicle industry, related organizations and governmental institutions to discover the true causes of road accidents and their consequences in investigations. Findings In this paper, the authors explored one of the potential effects of blockchain protocol on autonomous vehicles. The framework provides a solution for operating autonomous cars in an untrusted environment without needing a central authority. The model can also be generalized and applied to other intelligent unmanned systems. Originality/value This study proposes a blockchain protocol-based record-keeping model for autonomous cars to establish trusted parties in the traffic and protect single version of the truth.

scholarly journals Simulation Study of Autonomous Vehicles’ Effect on Traffic Flow Characteristics including Autonomous Buses

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Tanveer Muhammad ◽  
Faizan Ahmad Kashmiri ◽  
Hassan Naeem ◽  
Xin Qi ◽  
Hsu Chia-Chun ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Flow Rate ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Flow Characteristics ◽  
Transportation Industry ◽  
Lane Changing ◽  
Autonomous Cars ◽  
Ca Model ◽  
Multiple Scenarios

Autonomous vehicles are expected to revolutionize the transportation industry. The goal of this research is to study the heterogeneity in traffic flow dynamics by comparing different penetration rates of four different types of vehicles: autonomous cars (AC), autonomous buses (AB), manual cars (MC), and manual buses (MB). For the purpose of this research, a modified cellular automata (CA) model is developed in order to analyze the effect of heterogeneous vehicles (manual and autonomous). Previously, studies have focused on manual and autonomous cars, but we believe a gap in perception and analysis of mixed traffic still exists, as inclusion of other modes of autonomous vehicle research is very limited. Therefore, we have explicitly examined the effect of the AB on overall traffic flow. Moreover, two types of lane changing behavior (aggressive lane changing and polite lane changing) were also integrated into the model. Multiple scenarios through different compositions of vehicles were simulated. As per the results, if AB is employed concurrently with AC, there will be a significant improvement in traffic flow and road capacity, as equally more passengers can be accommodated in AB as AC is also anticipated to be used in carpooling. Secondly, when the vehicles change the lanes aggressively, there is a substantial growth in the flow rate and capacity of the network. Polite lane change does not significantly affect the flow rate.

Road Condition Based Adaptive Model Predictive Control for Autonomous Vehicles

2018 ◽  
Author(s):  
Xin Wang ◽  
Longxiang Guo ◽  
Yunyi Jia
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Fixed Cost ◽  
Control Performance ◽  
Road Condition ◽  
Adaptive Model Predictive Control ◽  
The Cost

Road conditions are of critical importance for motion control problems of the autonomous vehicle. In the existing studies of Model Predictive Control (MPC), road condition is generally modeled with the system dynamics, sometimes simplified as common disturbances, or even ignored based on some assumptions. For most of such MPC formulations, the cost function is usually designed as fixed function and has no relations with the time-varying road conditions. In order to comprehensively deal with the uncertain road conditions and improve the overall control performance, a new model predictive control strategy based on a mechanism of adaptive cost function is proposed in this paper. The relation between the cost function and road conditions is established based on a set of priority policies which reflect the different cost requirements under different road grades and friction coefficients. The adaptive MPC strategy is applied to solve the longitudinal control problem of autonomous vehicles. Simulation studies are conducted on the MPC method with both the fixed cost function and the adaptive cost function. The results show that the proposed adaptive MPC approach can achieve a better overall control performance under different road conditions.

scholarly journals Prediction of Steering Angle for Autonomous Vehicles Using Pre-Trained Neural Network

2021 ◽  
Vol 6 (5) ◽  
pp. 171-176
Author(s):  
Jonah Sokipriala
Keyword(s):  
Autonomous Vehicles ◽  
Mean Squared Error ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Research Area ◽  
Transportation Industry ◽  
Training Set ◽  
Steering Angle ◽  
Squared Error ◽  
Trained Neural Network

Autonomous driving is one promising research area that would not only revolutionize the transportation industry but would as well save thousands of lives. accurate correct Steering angle prediction plays a crucial role in the development of the autonomous vehicle .This research attempts to design a model that would be able to clone a drivers behavior using transfer learning from pretrained VGG16, the results showed that the model was able to use less training parameters and achieved a low mean squared error(MSE) of less than 2% without overfitting to the training set hence was able to drive on new road it was not trained on.

Transportation Planning for Connected Autonomous Vehicles: How It All Fits Together

2018 ◽  
Vol 2672 (51) ◽  
pp. 12-19 ◽  
Author(s):  
Bobby J. Cottam
Keyword(s):  
Transportation Planning ◽  
Urban Areas ◽  
Autonomous Vehicles ◽  
Planning Process ◽  
Transportation Network ◽  
Autonomous Vehicle ◽  
Vehicle To Vehicle ◽  
Potential System ◽  
Pilot Programs ◽  
Vehicle To Infrastructure

As connected and autonomous vehicle (CAV) technology continues to evolve and rapidly develop new capabilities, it is becoming increasingly important for transportation planners to consider the effects that these vehicles will have on the transportation network. It is evident that this trend has already started; over 60% of long-range transportation plans in the largest urban areas now include some discussion of CAVs, up from just 6% in 2015. There are also numerous CAV pilot programs currently underway that entail testing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction in both isolated and real-world environments. In this review of the current assessments for CAV impacts, two primary trends are identified. First, there is a great deal of uncertainty that is not being explicitly considered and properly accounted for in the transportation-network planning process. Second, the predictions that are being made are not considering potential policy or planning actions that could shape or affect the impacts of CAVs. This paper provides a picture of how ongoing CAV research interacts with current transportation planning practices by examining how the methods, the ranges of predictions, and the different sources of uncertainty in each method impact the planning process and potential system outcomes. Finally, it will identify best practices from decision analysis to help plan the best possible future transportation networks.

scholarly journals Intersection Control and Delay Optimization for Autonomous Vehicles Flows Only as Well as Mixed Flows with Ordinary Vehicles

Vehicles ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 523-541
Author(s):  
Abdullah Baz ◽  
Ping Yi ◽  
Ahmad Qurashi
Keyword(s):  
Traffic Safety ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Signalized Intersection ◽  
Mixed Traffic ◽  
Intersection Safety ◽  
Vehicle To Infrastructure ◽  
Intersection Control

The rapidly improving autonomous vehicle (AV) technology will have a significant impact on traffic safety and efficiency. This study introduces a game-theory-based priority control algorithm for autonomous vehicles to improve intersection safety and efficiency with mixed traffic. By using vehicle-to-infrastructure (V2I) communications, this model allows an AV to exchange information with the roadside units (RSU) to support the decision making of whether an ordinary vehicle (OV) or an AV should pass the intersection first. The safety of vehicles is taken in different stages of decisions to assure collision-free intersection operations. Two different mathematical models have been developed, where model one is for an AV/AV situation and model two is when an AV meets an OV. A simulation model was developed to implement the algorithm and compare the performance of each model with the conventional traffic control at a four-legged signalized intersection and at a roundabout. Three levels of traffic volume and speed combinations were tested in the simulation. The results show significant reductions in delay for both cases; for case (I), AV/AV model, a 65% reduction compared to a roundabout and 84% compared to a four-legged signalized intersection, and for case (II), AV/OV model, the reduction is 30% and 89%, respectively.

Developing the guidelines for managing autonomous vehicle flows on congested highways: A case study of MD-100

SIMULATION ◽  
2021 ◽  
pp. 003754972098687
Author(s):  
Ranteg S Rao ◽  
Sung Yoon Park ◽  
Gang-Len Chang
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Behavioral Patterns ◽  
Lane Changing ◽  
Traffic Conditions ◽  
Vehicle To Vehicle ◽  
Vehicle To Infrastructure ◽  
Negative Impacts ◽  
Behavioral Mechanisms ◽  
Measures Of Effectiveness

Recognizing the need for responsible highway agencies to effectively manage emerging autonomous vehicles (AV) flows in contending with daily recurrent congestion, this study presents a systematic procedure for understanding the impacts of AV flows on traffic conditions under different AV behavioral mechanisms (i.e., car-following and lane-changing), and different penetration rates. Research results show that the presence of AV flows, depending on their adopted behavioral mechanisms, may have significant (either positive or negative) impacts on the overall traffic conditions. Hence, it is essential for responsible highway agencies to have proper operational guidelines to manage and coordinate AV flows. To demonstrate the proposed methodology, this study has carried out extensive simulation experiments using a congested segment of the MD-100 network (a multilane highway segment located in Maryland) under various AV penetration rates and observable behavioral patterns. The collected Measures of Effectiveness highlight that at each AV penetration level there exists a set of optimal behavioral patterns for the AV flows to coordinate with non-AV flows via the Vehicle to Infrastructure or Vehicle to Vehicle infrastructure so as to maximize the roadway capacity and minimize the resulting highway congestion.

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.

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