1,615 research outputs found
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions
Ramp metering, a traditional traffic control strategy for conventional
vehicles, has been widely deployed around the world since the 1960s. On the
other hand, the last decade has witnessed significant advances in connected and
automated vehicle (CAV) technology and its great potential for improving
safety, mobility and environmental sustainability. Therefore, a large amount of
research has been conducted on cooperative ramp merging for CAVs only. However,
it is expected that the phase of mixed traffic, namely the coexistence of both
human-driven vehicles and CAVs, would last for a long time. Since there is
little research on the system-wide ramp control with mixed traffic conditions,
the paper aims to close this gap by proposing an innovative system architecture
and reviewing the state-of-the-art studies on the key components of the
proposed system. These components include traffic state estimation, ramp
metering, driving behavior modeling, and coordination of CAVs. All reviewed
literature plot an extensive landscape for the proposed system-wide coordinated
ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE
- ITSC 201
The urban real-time traffic control (URTC) system : a study of designing the controller and its simulation
The growth of the number of automobiles on the roads in China has put higher demands on the traffic control system that needs to efficiently reduce the level of congestion occurrence, which increases travel delay, fuel consumption, and air pollution. The traffic control system, urban real-time traffic control system based on multi-agent (MA-URTC) is presented in this thesis. According to the present situation and the traffic's future development in China, the researches on intelligent traffic control strategy and simulation based on agent lays a foundation for the realization of the system. The thesis is organized as follows: The first part focuses on the intersection' real-time signal control strategy. It contains the limitations of current traffic control systems, application of artificial intelligence in the research, how to bring the dynamic traffic flow forecast into effect by combining the neural network with the genetic arithmetic, and traffic signal real-time control strategy based on fuzzy control. The author uses sorne simple simulation results to testify its superiority. We adopt the latest agent technology in designing the logical structure of the MA-URTC system. By exchanging traffic flows information among the relative agents, MA-URTC provides a new concept in urban traffic control. With a global coordination and cooperation on autonomy-based view of the traffic in cities, MA-URTC anticipates the congestion and control traffic flows. It is designed to support the real-time dynamic selection of intelligent traffic control strategy and the real-time communication requirements, together with a sufficient level of fault-tolerance. Due to the complexity and levity of urban traffic, none strategy can be universally applicable. The agent can independently choose the best scheme according to the real-time situation. To develop an advanced traffic simulation system it can be helpful for us to find the best scheme and the best switch-point of different schemes. Thus we can better deal with the different real-time traffic situations. The second part discusses the architecture and function of the intelligent traffic control simulation based on agent. Meanwhile the author discusses the design model of the vehicle-agent, road agent in traffic network and the intersection-agent so that we can better simulate the real-time environment. The vehicle-agent carries out the intelligent simulation based on the characteristics of the drivers in the actual traffic condition to avoid the disadvantage of the traditional traffic simulation system, simple-functioned algorithm of the vehicles model and unfeasible forecasting hypothesis. It improves the practicability of the whole simulation system greatly. The road agent's significance lies in its guidance of the traffic participants. It avoids the urban traffic control that depends on only the traffic signal control at intersection. It gives the traffic participants the most comfortable and direct guidance in traveling. It can also make a real-time and dynamic adjustment on the urban traffic flow, thus greatly lighten the pressure of signal control in intersection area. To sorne extent, the road agent is equal to the pre-caution mechanism. In the future, the construction of urban roads tends to be more intelligent. Therefore, the research on road agent is very important. All kinds of agents in MA-URTC are interconnected through a computer network. In the end, the author discusses the direction of future research. As the whole system is a multi-agent system, the intersection, the road and the vehicle belongs to multi-agent system respectively. So the emphasis should be put on the structure design and communication of all kinds of traffic agents in the system. Meanwhile, as an open and flexible real-time traffic control system, it is also concerned with how to collaborate with other related systems effectively, how to conform the resources and how to make the traffic participants anywhere throughout the city be in the best traffic guidance at all times and places. To actualize the genuine ITS will be our final goal. \ud
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MOTS-CLÉS DE L’AUTEUR : Artificial Intelligence, Computer simulation, Fuzzy control, Genetic Algorithm, Intelligent traffic control, ITS, Multi-agent, Neural Network, Real-time
Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment
Recently, numerous studies have investigated cooperative traffic systems
using the communication among vehicle-to-everything (V2X). Unfortunately, when
multiple autonomous vehicles are deployed while exposed to communication
failure, there might be a conflict of ideal conditions between various
autonomous vehicles leading to adversarial situation on the roads. In South
Korea, virtual and real-world urban autonomous multi-vehicle races were held in
March and November of 2021, respectively. During the competition, multiple
vehicles were involved simultaneously, which required maneuvers such as
overtaking low-speed vehicles, negotiating intersections, and obeying traffic
laws. In this study, we introduce a fully autonomous driving software stack to
deploy a competitive driving model, which enabled us to win the urban
autonomous multi-vehicle races. We evaluate module-based systems such as
navigation, perception, and planning in real and virtual environments.
Additionally, an analysis of traffic is performed after collecting multiple
vehicle position data over communication to gain additional insight into a
multi-agent autonomous driving scenario. Finally, we propose a method for
analyzing traffic in order to compare the spatial distribution of multiple
autonomous vehicles. We study the similarity distribution between each team's
driving log data to determine the impact of competitive autonomous driving on
the traffic environment
Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models
The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied
on a sub-area of the road network of Rome and validated on the same floating car data set
Graph Reinforcement Learning Application to Co-operative Decision-Making in Mixed Autonomy Traffic: Framework, Survey, and Challenges
Proper functioning of connected and automated vehicles (CAVs) is crucial for
the safety and efficiency of future intelligent transport systems. Meanwhile,
transitioning to fully autonomous driving requires a long period of mixed
autonomy traffic, including both CAVs and human-driven vehicles. Thus,
collaboration decision-making for CAVs is essential to generate appropriate
driving behaviors to enhance the safety and efficiency of mixed autonomy
traffic. In recent years, deep reinforcement learning (DRL) has been widely
used in solving decision-making problems. However, the existing DRL-based
methods have been mainly focused on solving the decision-making of a single
CAV. Using the existing DRL-based methods in mixed autonomy traffic cannot
accurately represent the mutual effects of vehicles and model dynamic traffic
environments. To address these shortcomings, this article proposes a graph
reinforcement learning (GRL) approach for multi-agent decision-making of CAVs
in mixed autonomy traffic. First, a generic and modular GRL framework is
designed. Then, a systematic review of DRL and GRL methods is presented,
focusing on the problems addressed in recent research. Moreover, a comparative
study on different GRL methods is further proposed based on the designed
framework to verify the effectiveness of GRL methods. Results show that the GRL
methods can well optimize the performance of multi-agent decision-making for
CAVs in mixed autonomy traffic compared to the DRL methods. Finally, challenges
and future research directions are summarized. This study can provide a
valuable research reference for solving the multi-agent decision-making
problems of CAVs in mixed autonomy traffic and can promote the implementation
of GRL-based methods into intelligent transportation systems. The source code
of our work can be found at https://github.com/Jacklinkk/Graph_CAVs.Comment: 22 pages, 7 figures, 10 tables. Currently under review at IEEE
Transactions on Intelligent Transportation System
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