Cooperative Safety Applications for C-ITS Equipped and Non-equipped Vehicles Supported by an Extended Local Dynamic Map built on SAFE STRIP Technology

2019 ◽  
Author(s):  
Francesco Biral ◽  
Giammarco Valenti ◽  
Enrico Bertolazzi ◽  
Andrea Steccanella
Keyword(s):  
Local Dynamic ◽  
Dynamic Map ◽  
Safety Applications

scholarly journals Multi-Hop Dynamic Map Data Propagation Algorithm for Clustered Vehicular Networks

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1728
Author(s):  
Odilbek Urmonov ◽  
HyungWon Kim
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Dynamic Environment ◽  
Driving Safety ◽  
Local Dynamic ◽  
Vehicular Communication ◽  
Data Propagation ◽  
Dynamic Map ◽  
Map Data ◽  
Transmission Windows

To ensure the driving safety in vehicular network, it is necessary to construct a local dynamic map (LDM) for an extended range. Using the standard vehicular communication protocols, however, vehicles can construct the LDM for only one-hop range. Constructing large-scale LDM is highly challenging because vehicles randomly change their position. This paper proposes a dynamic map propagation (DMP) method, which builds a large aggregated LDM data using a multi-hop communication. To reduce the data overhead, we introduce an efficient clustering method based on a half-circle of the forwarder’s wireless range. The DMP elects one forwarder per cluster, which constructs LDM and forwards it to a neighbor cluster. The inter-cluster interference is minimized by allocating a different transmit window to each cluster. DMP copes with a dynamic environment by frequently re-electing the forwarders and their associated transmission windows. Simulation results reveal that DMP enhances the forwarders’ reception ratio by 20%, while extending LDM dissemination range by 29% over a previous work.

scholarly journals Implementation of a Sensor Big Data Processing System for Autonomous Vehicles in the C-ITS Environment

2020 ◽  
Vol 10 (21) ◽  
pp. 7858
Author(s):  
Aelee Yoo ◽  
Sooyeon Shin ◽  
Junwon Lee ◽  
Changjoo Moon
Keyword(s):  
Big Data ◽  
Autonomous Vehicles ◽  
Processing System ◽  
Intelligent Transport System ◽  
Local Dynamic ◽  
Intelligent Transport ◽  
The Road ◽  
Road Condition ◽  
Dynamic Map ◽  
Using Data

To provide a service that guarantees driver comfort and safety, a platform utilizing connected car big data is required. This study first aims to design and develop such a platform to improve the function of providing vehicle and road condition information of the previously defined central Local Dynamic Map (LDM). Our platform extends the range of connected car big data collection from OBU (On Board Unit) and CAN to camera, LiDAR, and GPS sensors. By using data of vehicles being driven, the range of roads available for analysis can be expanded, and the road condition determination method can be diversified. Herein, the system was designed and implemented based on the Hadoop ecosystem, i.e., Hadoop, Spark, and Kafka, to collect and store connected car big data. We propose a direction of the cooperative intelligent transport system (C-ITS) development by showing a plan to utilize the platform in the C-ITS environment.

scholarly journals Towards a Semantically Enriched Local Dynamic Map

2018 ◽  
Vol 17 (1) ◽  
pp. 32-48 ◽  
Author(s):  
Thomas Eiter ◽  
Herbert Füreder ◽  
Fritz Kasslatter ◽  
Josiane Xavier Parreira ◽  
Patrik Schneider
Keyword(s):  
Local Dynamic ◽  
Dynamic Map

scholarly journals Implementation and Evaluation of Local Dynamic Map in Safety Driving Systems

2015 ◽  
Vol 05 (02) ◽  
pp. 102-112 ◽  
Author(s):  
Hideki Shimada ◽  
Akihiro Yamaguchi ◽  
Hiroaki Takada ◽  
Kenya Sato
Keyword(s):  
Local Dynamic ◽  
Dynamic Map

Research of Three-Dimensional Layered Network Architecture LDM3 for ITS

2015 ◽  
Vol 719-720 ◽  
pp. 791-797
Author(s):  
Ya Duan Ruan ◽  
Xiang Jun Chen ◽  
Qi Mei Chen
Keyword(s):  
Information Fusion ◽  
Network Architecture ◽  
Intelligent Transportation System ◽  
Three Dimensional ◽  
Transport Layer ◽  
Massive Data ◽  
Local Dynamic ◽  
Application Layer ◽  
Application Systems ◽  
Dynamic Map

As Intelligent Transportation System (ITS) grows increasingly in size and complexity, the issues on how to improve interoperability and the performance of processing massive data become more critical. This paper proposes a new three-dimensional layered network architecture called Local Dynamic Map/Multimedia/Management (LDM3) to address these issues. In LDM3, the three-dimensional architecture consists of the information fusion layer newly introduced, as well as the application layer and transport layer. The primitive mechanism defined in the architecture improves the efficiency of the communication of network elements in such heterogeneous network system. By using the standardized format of message and data, the application systems can get desired information pushed by information fusion layer, instead of processing data from all kinds of sensors. This mechanism improves the effectiveness of the whole system significantly by avoiding duplicate work on different application systems. Meanwhile, the workload and the hardware requirement for application systems are relieved. We apply LDM3in the demonstration project for traffic Internet Of Things (IOT) in Jiangsu province. The result shows LDM3is an effective and efficient solution for ITS.

scholarly journals Selective local dynamic map construction based on MEC architecture

2019 ◽  
Vol 490 ◽  
pp. 062065
Author(s):  
Yunqiang Fan ◽  
Xinhong Wang ◽  
Fuqiang Liu ◽  
Ping Wang ◽  
Qingquan Zou ◽  
...  
Keyword(s):  
Local Dynamic ◽  
Map Construction ◽  
Dynamic Map

scholarly journals An Algorithm for Local Dynamic Map Generation for Safe UAV Navigation

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 88
Author(s):  
Jin-Woo Lee ◽  
Wonjai Lee ◽  
Kyoung-Dae Kim
Keyword(s):  
Operation Time ◽  
Ground Truth ◽  
Velocity Estimation ◽  
Sensor Data ◽  
Local Dynamic ◽  
Memory Usage ◽  
Operation Speed ◽  
Perception System ◽  
Dynamic Map ◽  
Uav Navigation

For safe UAV navigation and to avoid collision, it is essential to have accurate and real-time perception of the environment surrounding the UAV, such as free area detection and recognition of dynamic and static obstacles. The perception system of the UAV needs to recognize information such as the position and velocity of all objects in the surrounding local area regardless of the type of object. At the same time, a probability based representation taking into account the noise of the sensor is also essential. In addition, a software design with efficient memory usage and operation time is required in consideration of the hardware limitations of the UAVs. In this paper, we propose a 3D Local Dynamic Map (LDM) generation algorithm for a perception system for UAVs. The proposed LDM uses a circular buffer as a data structure to ensure low memory usage and fast operation speed. A probability based occupancy map is created using sensor data and the position and velocity of each object are calculated through clustering between grid voxels using the occupancy map and velocity estimation based on a particle filter. The objects are predicted using the position and velocity of each object and this is reflected in the occupancy map. This process is continuously repeated and the flying environment of the UAV can be expressed in a three-dimensional grid map and the state of each object. For the evaluation of the proposed LDM, we constructed simulation environments and the UAV for outdoor flying. As an evaluation factor, the occupancy grid is accuracy evaluated and the ground truth velocity and the estimated velocity are compared.

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