A Study on a Tracking and Lane Recognition Method for Nearby Vehicles Using 3D LiDAR and a High-Precision Digital Map

2019 ◽  
Vol 25 (5) ◽  
pp. 414-423
Author(s):  
Du-Hyeon Cho ◽  
Kyung-Jea Ahn ◽  
Yeonsik Kang
2005 ◽  
Vol 58 (2) ◽  
pp. 257-271 ◽  
Author(s):  
Mohammed A. Quddus ◽  
Robert B. Noland ◽  
Washington Y. Ochieng

Map Matching (MM) algorithms are usually employed for a range of transport telematics applications to correctly identify the physical location of a vehicle travelling on a road network. Two essential components for MM algorithms are (1) navigation sensors such as the Global Positioning System (GPS) and dead reckoning (DR), among others, to estimate the position of the vehicle, and (2) a digital base map for spatial referencing of the vehicle location. Previous research by the authors (Quddus et al., 2003; Ochieng et al., 2003) has developed improved MM algorithms that take account of the vehicle speed and the error sources associated with the navigation sensors and the digital map data previously ignored in conventional MM approaches. However, no validation study assessing the performance of MM algorithms has been presented in the literature. This paper describes a generic validation strategy and results for the MM algorithm previously developed in Ochieng et al. (2003). The validation technique is based on a higher accuracy reference (truth) of the vehicle trajectory as determined by high precision positioning achieved by the carrier-phase observable from GPS. The results show that the vehicle positions determined from the MM results are within 6 m of the true positions. The results also demonstrate the importance of the quality of the digital map data to the map matching process.


2011 ◽  
Vol 17 (1) ◽  
pp. 47-53 ◽  
Author(s):  
Woo-Yong Kang ◽  
Eun-Sung Lee ◽  
Geon-Woo Lee ◽  
Jae-Ik Park ◽  
Kwang-Sik Choi ◽  
...  
Keyword(s):  

2020 ◽  
Vol 10 (18) ◽  
pp. 6622
Author(s):  
Ziyu Zhao ◽  
Lin Bi

During the operation of open-pit mining, the loading position of a haulage truck often changes, bringing a new challenge concerning how to plan an optimal truck transportation path considering the terrain factors. This paper proposes a path planning method based on a high-precision digital map. It contains two parts: (1) constructing a high-precision digital map of the cutting zone and (2) planning the optimal path based on the modified Hybrid A* algorithm. Firstly, we process the high-precision map based on different terrain feature factors to generate the obstacle cost map and surface roughness cost map of the cutting zone. Then, we fuse the two cost maps to generate the final cost map for path planning. Finally, we incorporate the contact cost between tire and ground to improve the node extension and path smoothing part of the Hybrid A* algorithm and further enhance the algorithm’s capability of avoiding the roughness. We use real elevation data with different terrain resolutions to perform random tests and the results show that, compared with the path without considering the terrain factors, the total transportation cost of the optimal path is reduced by 10%–20%. Moreover, the methods demonstrate robustness.


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