Self-Driving Robotic Cars

2022 ◽  
pp. 969-1001
Author(s):  
Jelena L. Pisarov ◽  
Gyula Mester
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicle ◽  
Closed Circuit ◽  
Small Scale ◽  
Traffic Patterns ◽  
Complex Environments ◽  
The Road ◽  
Traffic Jams ◽  
Simulation Techniques ◽  
On The Road

Even the behavior of a single driver can have a dramatic impact on hundreds of cars, making it more difficult to manage traffic. While the attempts to analyze and correct the traffic patterns that lead to congestion began as early in the 1930s, it wasn't until recently that scientists developed simulation techniques and advanced algorithms to create more realistic visualizations of traffic flow. In experiments conducted by Alexandre Bayen and the Liao-Cho, which included several dozen cars in a small-scale closed circuit, a single autonomous vehicle could eliminate traffic jams by moderating the speed of every car on the road. In larger simulations, the research showed that once their number rises to 5-10% of all cars in the traffic, they can manage localized traffic even in complex environments, such as merging multiple lanes of traffic into two or navigating extremely busy sections.

Self-Driving Robotic Cars

2021 ◽  
pp. 599-631
Author(s):  
Jelena L. Pisarov ◽  
Gyula Mester
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicle ◽  
Closed Circuit ◽  
Small Scale ◽  
Traffic Patterns ◽  
Complex Environments ◽  
The Road ◽  
Traffic Jams ◽  
Simulation Techniques ◽  
On The Road

Even the behavior of a single driver can have a dramatic impact on hundreds of cars, making it more difficult to manage traffic. While the attempts to analyze and correct the traffic patterns that lead to congestion began as early in the 1930s, it wasn't until recently that scientists developed simulation techniques and advanced algorithms to create more realistic visualizations of traffic flow. In experiments conducted by Alexandre Bayen and the Liao-Cho, which included several dozen cars in a small-scale closed circuit, a single autonomous vehicle could eliminate traffic jams by moderating the speed of every car on the road. In larger simulations, the research showed that once their number rises to 5-10% of all cars in the traffic, they can manage localized traffic even in complex environments, such as merging multiple lanes of traffic into two or navigating extremely busy sections.

THE EFFECT OF DECELERATION STRIPS UPON TRAFFIC FLOW

2006 ◽  
Vol 20 (14) ◽  
pp. 835-841 ◽  
Author(s):  
JIAN-PING MENG ◽  
JIE-FANG ZHANG
Keyword(s):  
Traffic Flow ◽  
Periodic Boundary Condition ◽  
Periodic Boundary ◽  
Maximum Flow ◽  
Small Scale ◽  
Traffic Jam ◽  
The Road ◽  
Traffic Jams ◽  
Vehicle Density ◽  
On The Road

We propose a new single-lane cellular automaton model to study the effect of deceleration strips upon traffic flow. By performing numerical simulations under the periodic boundary condition, we find that the deceleration strips could be considered as the headstream of traffic jams and they are apt to cause the small scale traffic jam. The maximum flow is decreased evidently when there are some deceleration strips on the road. But the increase of the number of deceleration strips only influences the maximum flow slightly. Furthermore, if the vehicle density is high enough, the influence of deceleration strips could be ignored. Therefore, the disadvantage of the deceleration strips may be ignored in city traffic because of the high density.

scholarly journals Quantifying the phantom jam externality: the case of an Autobahn section in Germany

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Kathrin Goldmann ◽  
Gernot Sieg
Keyword(s):  
Traffic Flow ◽  
Traffic Demand ◽  
Traffic Jam ◽  
The Road ◽  
Traffic Jams ◽  
On The Road ◽  
High Road

AbstractIf not restricted by tolls, private decisions to drive on a highway result in inefficiently high usage which leads to traffic jams. When traffic demand is high, traffic jams can occur simply because of the interaction of vehicle drivers on the road, a phenomenon called phantom jam. The probability of phantom jams occurring increases with traffic flow. Unpriced externalities lead to inefficiently high road usage. We offer a method for quantifying traffic jam externalities and identifying and isolating the phantom jam externality. We examine the method by applying it to a specific highway section in Germany. The maximal congestion externality for the analyzed highway section is about 38 cents per vehicle and kilometer. Congestion charges that are calculated ignoring phantom jam externalities, can only internalize two-thirds of the true externality.

scholarly journals New Operating Strategies for an On-the-Road Modular, Electric and Autonomous Vehicle Concept in Urban Transportation

2019 ◽  
Vol 10 (4) ◽  
pp. 91
Author(s):  
Christian Ulrich ◽  
Horst E. Friedrich ◽  
Jürgen Weimer ◽  
Stephan A. Schmid
Keyword(s):  
Business Model ◽  
Urban Areas ◽  
Business Models ◽  
Autonomous Vehicle ◽  
Vehicle Concept ◽  
Drive Unit ◽  
The Road ◽  
Business Model Canvas ◽  
Operating Strategies ◽  
On The Road

Today commercial transport in urban areas faces major challenges. These include making optimal use of limited space, avoiding empty trips, meeting driver shortages as well as reducing costs and emissions such as CO2, particulate matter and noise. The mutual acceleration and reinforcement of technological trends such as electrification, digitization and automation may enable new vehicle and mobility concepts that can meet these challenges. One possible vehicle concept is presented in this article. It is based on on-the-road modularization, i.e., a vehicle that can change different transport capsules during operation. The vehicle is divided into an electrically propelled autonomous drive unit and a transport unit. Standardized interfaces between these units enable the easy design of capsules for different uses, while the drive unit can be used universally. Business models and operating strategies that allow optimal use of this vehicle concept are discussed in depth in the article. First, the current situation is analyzed followed by a detailed description of an exemplary business model using a business model canvas. The operating strategies and logistics concepts are illustrated and compared with conventional concepts.

scholarly journals Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4719
Author(s):  
Malik Haris ◽  
Jin Hou
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Research Area ◽  
Visual Navigation ◽  
Obstacle Detection ◽  
The Road ◽  
Markov Random ◽  
Active Research ◽  
On The Road ◽  
Active Research Area

Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.

scholarly journals On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability

AI ◽  
2020 ◽  
Vol 1 (4) ◽  
pp. 558-585
Author(s):  
Michael Broome ◽  
Matthew Gadd ◽  
Daniele De Martini ◽  
Paul Newman
Keyword(s):  
Autonomous Vehicle ◽  
Optimal Path ◽  
Extreme Environments ◽  
Point Clouds ◽  
Learning Approaches ◽  
Weak Supervision ◽  
The Road ◽  
Optimal Path Planning ◽  
Rule Set ◽  
On The Road

This is motivated by a requirement for robust, autonomy-enabling scene understanding in unknown environments. In the method proposed in this paper, discriminative machine-learning approaches are applied to infer traversability and predict routes from Frequency-Modulated Contunuous-Wave (FMCV) radar frames. Firstly, using geometric features extracted from LiDAR point clouds as inputs to a fuzzy-logic rule set, traversability pseudo-labels are assigned to radar frames from which weak supervision is applied to learn traversability from radar. Secondly, routes through the scanned environment can be predicted after they are learned from the odometry traces arising from traversals demonstrated by the autonomous vehicle (AV). In conjunction, therefore, a model pretrained for traversability prediction is used to enhance the performance of the route proposal architecture. Experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community. Our key finding is that joint learning of traversability and demonstrated routes lends itself best to a model which understands where the vehicle should feasibly drive. We show that the traversability characteristics can be recovered satisfactorily, so that this recovered representation can be used in optimal path planning, and that an end-to-end formulation including both traversability feature extraction and routes learned by expert demonstration recovers smooth, drivable paths that are comprehensive in their coverage of the underlying road network. We conclude that the proposed system will find use in enabling mapless vehicle autonomy in extreme environments.

scholarly journals FOURIER ANALYSIS OF THE TRAFFIC FLOW INTENSITY

2021 ◽  
pp. 46-59
Author(s):  
M.G. Boyarshinov ◽  
◽  
A.S. Vavilin ◽  
A.G. Shumkov ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Fourier Series ◽  
Traffic Flow ◽  
Harmonic Functions ◽  
Traffic Flows ◽  
The Road ◽  
Flow Intensity ◽  
Software And Hardware ◽  
On The Road ◽  
Amplitude Characteristics

The relevance of this work is determined by the need to find modern ways to process the information about traffic flows for regulating and controlling the movement of transport and pedestrians, to reduce congestion, road accidents, etc. The object of study is a part of road with heavy two-way traffic, equipped with a software and hardware complex that allows to measure the characteristics of the transport flow. The subject of the study is the daily intensity of the cars flow during the week, from Monday to Sunday. The purpose of this study is to analyze the amplitudes, frequencies, and periods of harmonic functions obtained by decomposing the time series of road traffic intensities to identify the main patterns of traffic flow formation. As a theoretical and methodological approach, the decomposition of the function of the traffic flow intensity in the Fourier series with respect to harmonic functions is used. The approach developed by the authors using the fast Fourier transform procedure made it possible to determine the amplitude-frequency characteristics of the time series under consideration, which is a scientific novelty of the analysis. It is proposed to use the «period-amplitude» characteristics as physically more meaningful instead of the «frequency-amplitude» dependencies traditionally used for the analysis. The processing of data obtained from software and hardware complexes allowed us to determine dependences of the car flow intensity on the road of the Perm city at different averaging intervals, to describe the features of the motor transport movement on the road under consideration. As a result of the study, the amplitude-frequency characteristics of time series are obtained. It is shown that the individual harmonics of the Fourier series expansion of the traffic flow intensity, which exhibits the properties of a random function, duplicate the periodicity of the global, local, and intermediate extremes of the original function and have similar periods. The practical significance consists in the use of the decomposition of the function of the traffic flow intensity in the Fourier series of harmonic functions for predicting traffic flows, controlling the operation of traffic lights, monitoring the operation of equipment, as well as in the reconstruction, design and construction of roads and road objects. The study will continue in the direction of obtaining, processing and determining the «period-amplitude» characteristics for time series of traffic flow intensity for other road networks.

Self-Driving Cars

2020 ◽  
pp. 490-507
Author(s):  
Pooja Jha ◽  
K. Sridhar Patnaik
Keyword(s):  
Traffic Safety ◽  
Autonomous Vehicle ◽  
Technological Advancement ◽  
Social Environments ◽  
Highway Traffic ◽  
National Highway Traffic Safety ◽  
Human Errors ◽  
The Road ◽  
On The Road ◽  
Self Driving Cars

Human errors are the main cause of vehicle crashes. Self-driving cars bear the promise to significantly reduce accidents by taking the human factor out of the equation, while in parallel monitor the surroundings, detect and react immediately to potentially dangerous situations and driving behaviors. Artificial intelligence tool trains the computers to do things like detect lane lines and identify cyclists by showing them millions of examples of the subject at hand. The chapter in this book discusses the technological advancement in transportation. It also covers the autonomy used according to The National Highway Traffic Safety Administration (NHTSA). The functional architecture of self-driving cars is further discussed. The chapter also talks about two algorithms for detection of lanes as well as detection of vehicles on the road for self-driving cars. Next, the ethical discussions surrounding the autonomous vehicle involving stakeholders, technologies, social environments, and costs vs. quality have been discussed.

Construction of Control Algorithm for an Autonomous Vehicle

2001 ◽  
Vol 13 (4) ◽  
pp. 387-394
Author(s):  
Hyung-Eun Im ◽  
◽  
Ichiro Kageyama ◽  
Yoshiyuki Nozaki
Keyword(s):  
Control Algorithm ◽  
Prediction Models ◽  
Autonomous Vehicle ◽  
Risk Level ◽  
Minimum Risk ◽  
Control Motion ◽  
Risk Elements ◽  
The Road ◽  
On The Road ◽  
Risk Potential

In this study, a control algorithm of an autonomous vehicle is proposed on the basis of risk level to simulate control motion of a real driver. The normal traffic situation can be expressed by risk level. The risk level is affected by several risk elements: roadside edges, curves, the other vehicles, obstacles, and so on. Each risk element is represented by an exponential function. The risk elements make risk potential field on the road. It is assumed that the desirable course to follow is determined as the point of minimum risk potential in the cross section of the road. Tree prediction models are examined to predict the future position of vehicle. The change of preview time is considered on the curved road. A lateral and longitudinal control algorithm with the prediction model proposed in this study shows similar control motion to that of a real driver.

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