The SMT-based automatic road network generation in vehicle simulation environment

2016 ◽  
Author(s):  
BaekGyu Kim ◽  
Akshay Jarandikar ◽  
Jonathan Shum ◽  
Shinichi Shiraishi ◽  
Masahiro Yamaura
Keyword(s):  
Road Network ◽  
Simulation Environment ◽  
Network Generation ◽  
Vehicle Simulation

scholarly journals Development of High Precision Vehicle Dynamic Model with an Intelligent Torque Transfer System (All-Wheel Drive System)

2018 ◽  
Vol 167 ◽  
pp. 02011
Author(s):  
Beom-Joon Pyun ◽  
Chul-Woo Moon ◽  
Chang-Hyun Jeong ◽  
Do-Hyun Jung
Keyword(s):  
Control System ◽  
High Precision ◽  
Intelligent System ◽  
Vehicle Stability ◽  
Transfer System ◽  
Test Results ◽  
Simulation Environment ◽  
Control Logic ◽  
Vehicle Simulation ◽  
Full Vehicle

High precision vehicle simulation environment is required for development of control system of any newly suggested intelligent system. Hence, a high precision full-vehicle simulation environment integrated with an intelligent torque transfer system should be developed for an advanced control logic for enhancement of vehicle stability. In the perspective of making enhanced AWD system, there are many kinds of methods to make the system. And a controller part of the AWD module is regarded as a major part of the system development in consideration of enhancement of the vehicle stability with the suggested AWD system. Therefore, in this study, high precision full-vehicle simulation environment is developed for the development of an intelligent control system of the AWD module. In order to make models for the simulation, vehicle test is performed with a commercial vehicle, and the several performance tests of the developed AWD system are also conducted in a laboratory. Then, the simulation environment comprised of several models of important sub-systems is developed based on the previously conducted test results, and the developed simulation environment is verified by comparing the simulation results to the test results.

scholarly journals An Intersection-First Approach for Road Network Generation from Crowd-Sourced Vehicle Trajectories

2019 ◽  
Vol 8 (11) ◽  
pp. 473 ◽  
Author(s):  
Caili Zhang ◽  
Longgang Xiang ◽  
Siyu Li ◽  
Dehao Wang
Keyword(s):  
Road Network ◽  
Rapid Development ◽  
Road Networks ◽  
Uneven Distribution ◽  
Trajectory Data ◽  
Network Generation ◽  
The Road ◽  
Lower Accuracy ◽  
Road Intersections ◽  
Vehicle Trajectory

Extracting highly detailed and accurate road network information from crowd-sourced vehicle trajectory data, which has the advantages of being low cost and able to update fast, is a hot topic. With the rapid development of wireless transmission technology, spatial positioning technology, and the improvement of software and hardware computing ability, more and more researchers are focusing on the analysis of Global Positioning System (GPS) trajectories and the extraction of road information. Road intersections are an important component of roads, as they play a significant role in navigation and urban planning. Even though there have been many studies on this subject, it remains challenging to determine road intersections, especially for crowd-sourced vehicle trajectory data with lower accuracy, lower sampling frequency, and uneven distribution. Therefore, we provided a new intersection-first approach for road network generation based on low-frequency taxi trajectories. Firstly, road intersections from vector space and raster space were extracted respectively via using different methods; then, we presented an integrated identification strategy to fuse the intersection extraction results from different schemes to overcome the sparseness of vehicle trajectory sampling and its uneven distribution; finally, we adjusted road information, repaired fractured segments, and extracted the single/double direction information and the turning relationships of the road network based on the intersection results, to guarantee precise geometry and correct topology for the road networks. Compared with other methods, this method shows better results, both in terms of their visual inspections and quantitative comparisons. This approach can solve the problems mentioned above and ensure the integrity and accuracy of road intersections and road networks. Therefore, the proposed method provides a promising solution for enriching and updating navigable road networks and can be applied in intelligent transportation systems.

scholarly journals Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

2019 ◽  
Vol 11 (16) ◽  
pp. 4511 ◽  
Author(s):  
Ling Zheng ◽  
Bijun Li ◽  
Bo Yang ◽  
Huashan Song ◽  
Zhi Lu
Keyword(s):  
Mathematical Modeling ◽  
Autonomous Vehicles ◽  
Road Network ◽  
Rapid Development ◽  
Autonomous Driving ◽  
Extraction Methods ◽  
Network Data ◽  
Network Generation ◽  
Fundamental Part ◽  
Road Geometry

Autonomous driving is experiencing rapid development. A lane-level map is essential for autonomous driving, and a lane-level road network is a fundamental part of a lane-level map. A large amount of research has been performed on lane-level road network generation based on various on-board systems. However, there is a lack of analysis and summaries with regards to previous work. This paper presents an overview of lane-level road network generation techniques for the lane-level maps of autonomous vehicles with on-board systems, including the representation and generation of lane-level road networks. First, sensors for lane-level road network data collection are discussed. Then, an overview of the lane-level road geometry extraction methods and mathematical modeling of a lane-level road network is presented. The methodologies, advantages, limitations, and summaries of the two parts are analyzed individually. Next, the classic logic formats of a lane-level road network are discussed. Finally, the survey summarizes the results of the review.

scholarly journals Incremental Road Network Generation Based on Vehicle Trajectories

2018 ◽  
Vol 7 (10) ◽  
pp. 382 ◽  
Author(s):  
Zhongyi Ni ◽  
Lijun Xie ◽  
Tian Xie ◽  
Binhua Shi ◽  
Yao Zheng
Keyword(s):  
Real Time ◽  
Road Network ◽  
Trajectory Data ◽  
Network Generation ◽  
The Real ◽  
The Road ◽  
Vehicle Trajectories ◽  
Representation Point ◽  
Road Planning ◽  
Vehicle Trajectory

Nowadays, most vehicles are equipped with positioning devices such as GPS which can generate a tremendous amount of trajectory data and upload them to the server in real time. The trajectory data can reveal the shape and evolution of the road network and therefore has an important value for road planning, vehicle navigation, traffic analysis, and so on. In this paper, a road network generation method is proposed based on the incremental learning of vehicle trajectories. Firstly, the input vehicle trajectory data are cleaned by a preprocess module. Then, the original scattered positions are clustered and mapped to the representation points which stand for the feature points of the real roads. After that, the corresponding representation points are connected based on the original connection information of the trajectories. Finally, all representation points are connected by a Delaunay triangulation network and the real road segments are found by a shortest path searching approach between the connected representation point pairs. Experiments show that this method can build the road network from scratch and refine it with the input data continuously. Both the accuracy and timeliness of the extracted road network can continuously be improved with the growth of real-time trajectory data.

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