Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection

Yudin, Dmitry; Skrynnik, Alexey; Krishtopik, Andrey; Belkin, Ilya; Panov, Aleksandr I.

doi:10.3103/s1060992x19040118

Cited by 35 publications

(16 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The big data and smart city research team has made systematic research in related fields from big data application in urban planning, urban and rural planning, and design based on big data to urban and rural special planning and urban and rural evaluation management based on big data [ 15 ]. The researchers worked on planning support systems and microsimulation of urban systems and successfully analyzed the spatiotemporal distribution characteristics of commuting trips using bus IC card swiping data, mobile phone data, and Weibo data [ 16 ].…”

Section: Related Workmentioning

confidence: 99%

Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This paper analyzes and studies the structure and parameters of the VGGNet network model and selects the most commonly used and efficient VGG-16 as the prototype of the improved model. A multiscale sampling layer is added at the end of the VGG-16 convolution part so that the model can input images of any size for training and testing while reducing the number of neurons in the fully connected layer. This improves the training speed of the model under the premise of ensuring the accuracy. This paper uses multisource street spatial data combined with geographic information spatial analysis technology to measure and evaluate the spatial quality of streets in the main urban area. From the three dimensions of vitality, safety, and greenness of urban street space quality, a systematic structure for evaluation and analysis of street space quality is constructed. Street vitality includes eight index factors: entrance and exit density, street furniture density, street sketch density, street characteristic landscape density, POI density, POI diversity, commercial POI ratio, and street population density. There are five index factors: degree, roadside parking occupancy ratio, traffic signal system density, sidewalk width proportion, and street facility density. We use ArcGIS to build an index factor information database for statistical analysis and visualization. According to the natural discontinuous point classification method, the safety level of urban street public space is divided into five grades. The sample size of the first four grades has a small fluctuation range. The sample sizes are 153, 172, 153, and 158, respectively, accounting for 21%, 23%, 21%, and 21% of the total street samples, of which the first two grades occupy a total of 44%, so 44% of the streets in the main urban area have a low-quality level of street space. Level 5 has a sample of 102 streets, accounting for 14%, with an average street space quality value of 0.43.

show abstract

Section: Related Workmentioning

confidence: 99%

Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Aerial photographs can provide visual information about the road intersection to create a simulated model. The vehicles' position, both autonomous and manned, can be detected using deep neural networks of various kinds [33]. Vehicles in the intersection can be detected with Simulation models for AVs at intersections can also be trained thanks to machine learning, specifically Reinforcement Learning (RL) and computer vision.…”

Section: Marking Type Pavement Markingmentioning

confidence: 99%

“…Aerial photographs can provide visual information about the road intersection to create a simulated model. The vehicles' position, both autonomous and manned, can be detected using deep neural networks of various kinds [33]. Vehicles in the intersection can be detected with the segmentation technique, starting from a full-color aerial image of the crossroad captured with drones and then post-processed, moving through computerized Fully Convolutional Network (FCN), creating a B/W image which then becomes clustered through a DBSCAN.…”

Section: Marking Type Pavement Markingmentioning

confidence: 99%

Autonomous Vehicles: An Analysis Both on Their Distinctiveness and the Potential Impact on Urban Transport Systems

et al. 2021

View full text Add to dashboard Cite

Autonomous driving is a technological innovation that involves the use of Artificial Intelligence (AI) in the automotive area, representing the future of transport and whose applications will influence the concept of driving and many other features of modern society. Indeed, the introduction of Autonomous Vehicles (AVs) on the market, along with the development of related technologies, will have a potential impact not only on the automotive industry but also on urban transport systems. New mobility-related businesses will emerge, whereas existing ones will have to adapt to changes. There are various aspects that affect urban transport systems: in this work, we highlight how road markings, intersections, and pavement design upgradings have a key role for AVs operation. This work aims to describe how contemporary society will be influenced by Autonomous Vehicles’ spread in regard to urban transport systems. A comprehensive analysis of the expected developments within urban transport systems is hereby presented, and some crucial issues concerning benefits and drawbacks are also discussed. From our studies, it emerges that the detection performed by vehicles is mainly affected by road markings characteristics, especially at intersections. Indeed, the need for a new cross-sections type arise, since vehicles wandering phenomena will be reduced due to AVs position-keeping systems.

show abstract

“…Tran and Bae [43] also presented a deep RL-based model that considers the effectiveness of leading autonomous vehicles in mixed-autonomy traffic at a non-signalized intersection. Yudin et al [44] offered a new approach to training the intelligent agent that simulates the behavior of an unmanned vehicle, based on the integration of reinforcement learning and computer vision. Using full visual information about the road intersection obtained from aerial photographs, they studied through automatic detection the relative positions of all road agents with various architectures of deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections

Németh

Gáspár

2021

Applied Sciences

View full text Add to dashboard Cite

The design of the motion of autonomous vehicles in non-signalized intersections with the consideration of multiple criteria and safety constraints is a challenging problem with several tasks. In this paper, a learning-based control solution with guarantees for collision avoidance is proposed. The design problem is formed in a novel way through the division of the control problem, which leads to reduced complexity for achieving real-time computation. First, an environment model for the intersection was created based on a constrained quadratic optimization, with which guarantees on collision avoidance can be provided. A robust cruise controller for the autonomous vehicle was also designed. Second, the environment model was used in the training process, which was based on a reinforcement learning method. The goal of the training was to improve the economy of autonomous vehicles, while guaranteeing collision avoidance. The effectiveness of the method is presented through simulation examples in non-signalized intersection scenarios with varying numbers of vehicles.

show abstract

Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection

Cited by 35 publications

References 4 publications

Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network

Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network

Autonomous Vehicles: An Analysis Both on Their Distinctiveness and the Potential Impact on Urban Transport Systems

The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections

Contact Info

Product

Resources

About