A Novel Urban Emergency Path Planning Method Based on Vector Grid Map

Yang, Bowen; Ding, Zhiming; Yuan, Lei; Jin, Yan; Guo, Limin; Cai, Zhi

doi:10.1109/access.2020.3018729

Cited by 16 publications

(9 citation statements)

References 33 publications

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“…Yang et al [21] introduced a grid map emergency path planning (GMEPP) framework that uses a grid road network for emergency vehicle driving systems. The road-related data is divided into grids, and the edges are identified for further analysis.…”

Section: Related Workmentioning

confidence: 99%

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Anjum

Shahab

2023

Sustainability

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Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics.

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Section: Related Workmentioning

confidence: 99%

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Anjum

Shahab

2023

Sustainability

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“…The red indicates the target point, and the gray indicates the obstacle. In the computer, the map will be converted into a two-dimensional array, 0 and 1 respectively representing the walkable area and the obstacle area [5](Figure 1).…”

Section: Grid Map 21 Definition Of Grid Mapmentioning

confidence: 99%

AI intelligent wayfinding based on Unreal Engine 4 static map

Xie

Chen

Wang

et al. 2022

J. Phys.: Conf. Ser.

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The development of games is closely related to the development of game engines. Unreal Engine is a mainstream game development engine. Unreal Engine has a powerful lighting rendering function and blueprint programming system.Therefore, using Unreal Engine for game development will better meet the requirements of modern game users for game quality and game content. In games, the AI path-finding system is often the core of a game. However, the path-finding component of the unreal engine is usually only applies to three-dimensional game and is used for intelligent path-finding in dynamic environment. Howerve, the 2D game usually finds the way in static environment. At present, there are a lareg number of path-finding algorithms and theoretical studies based on static environment, but there is no simulation experiment and detailed explanation of intelligent path-finding in static environment based on the Unreal Engine platform. This article firstly explains the principle of path-finding algorithms that are several commonly used in static environment. Then it compares the performance of different algorithms, and selects the optimal solution among several schemes by summarizing the experimental results. Then based on the optimal path-finding algorithm, using blueprint programming to implement AI path-finding in the Unreal engine. Finally, this article sort out and summarize the overall work process and indicate direction for future optimization.

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“…e node-segment model is a common method that simplifies the path planning problem while preserving the characteristics of the road networks [13][14][15]. e model is composed of nodes and road segments, which especially focuses on the joint point of roads, where vehicle can change path, such as the connection between the main road and side road in the same direction, and the intersections that connect opposite roads are regarded as nodes in the model.…”

Section: Establishment Of Congested Urban Arterial Roadmentioning

confidence: 99%

“…A great number of research studies focus on vehicle path planning problems. Yang et al proposed a path planning method for emergency vehicles, where the road network is divided into a weighted grid and then the rescue path is planned by vector grid map method [13].…”

Section: Introductionmentioning

confidence: 99%

Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach

Yan

Wang

Yang

et al. 2021

Journal of Advanced Transportation

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Fast road emergency response can minimize the losses caused by traffic accidents. However, emergency rescue on urban arterial roads is faced with the high probability of congestion caused by accidents, which makes the planning of rescue path complicated. This paper proposes a refined path planning method for emergency rescue vehicles on congested urban arterial roads during traffic accidents. Firstly, a rescue path planning environment for emergency vehicles on congested urban arterial roads based on the Markov decision process is established, which focuses on the architecture of arterial roads, taking the traffic efficiency and vehicle queue length into consideration of path planning; then, the prioritized experience replay deep Q-network (PERDQN) reinforcement learning algorithm is used for path planning under different traffic control schemes. The proposed method is tested on the section of East Youyi Road in Xi’an, Shaanxi Province, China. The results show that compared with the traditional shortest path method, the rescue route planned by PERDQN reduces the arrival time to the accident site by 67.1%, and the queue length at upstream of the accident point is shortened by 16.3%, which shows that the proposed method is capable to plan the rescue path for emergency vehicles in urban arterial roads with congestion, shorten the arrival time, and reduce the vehicle queue length caused by accidents.

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A Novel Urban Emergency Path Planning Method Based on Vector Grid Map

Cited by 16 publications

References 33 publications

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

AI intelligent wayfinding based on Unreal Engine 4 static map

Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach

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