Global positioning system (GPS) trajectory map matching projects GPS coordinates to the road network. Most existing algorithms focus on the geometric and topological relationships of the road network, while did not make full use of the historical road network information and floating car data. In this study, the authors proposed a deep learning enabled vehicle trajectory map-matching method with advanced spatial–temporal analysis (DST-MM). The algorithm mainly focused on the following three aspects: (i) analyse the spatial relevancy from the prospective of geometric analysis, topology analysis and intersection analysis; (ii) to make full use of the historical and real-time data, a deep learning model was conducted to extract the road network and vehicle trajectory features and (iii) establish a speed prediction model and nest it in the temporal analysis structure. It narrows down the path search range through establishing the dynamic candidate graph. Experimental results show that the proposed DST-MM algorithm outperforms the existing algorithms in terms of matching accuracy for low-sampling frequencies GPS data, especially in the central urban area.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2020.0486

Deep learning enabled vehicle trajectory map-matching method with advanced spatial–temporal analysis

This paper presents new algorithmic methods for accuracy improvement of autonomous inertial navigation systems of aircrafts. Firstly, an inertial navigation system platform and its nonlinear error model are considered, and the correction schemes are presented for autonomous inertial navigation systems using internal information. Next, a correction algorithm is proposed based on signals from precession angle sensors. A vector of reduced measurements for the estimation algorithm is formulated using the information about the angles of precession. Finally, the accuracy of the developed correction algorithms for autonomous inertial navigation systems of aircrafts is studied. Numerical solutions for the correction algorithm are presented by the adaptive Kalman filter for the measurement data from the sensors. Real data of navigation system Ts-060K are obtained in laboratory experiments, which validates the proposed algorithms.

https://www.mdpi.com/1424-8220/19/22/5016/pdf

New Algorithms for Autonomous Inertial Navigation Systems Correction with Precession Angle Sensors in Aircrafts.

The paper presents a map-matching method which mainly considers the curvature integral value of the curve as a map-matching characteristic for constraining the associated matching between two adjacent GPS track points.Through the implementation of map matching experiments for floating car data on the different conditions of both route categories and sampling intervals,the proposed curvature integration constrained mapmatching method could be superior to the classic floating car map matching method when evaluating them by the matching accuracy and stability.

Curvature Integration Constrained Map Matching Method for GPS Floating Car Data

Owing to the fascinating structural, optical, electrical, chemical properties, graphene has created new paradigm in the field of nanoscience and the common crystalline structures that can be exfoliated include the layered van der Waals (vdW) solids such as boron nitride, transition metal dichalcogenides (TMDCs), black phosphorus, and the layered ionic solids. Here, we bring forth the state-of-art-of materials dominated by their two-dimensional (2D) geometry beyond graphene. Being one of the most well-studied families of vdW layered materials, molybdenum disulphide (MoS2) belonging to TMDC family has gained considerable research interest. The present work is focused on attempts to optimize and characterize this material with unique properties for a host of applications. The work resolves the hydrothermal growth of hexagonal MoS2 nanoflakes with attracting optical and magnetic properties providing strong evidence for the spin orbit split valence bands of these nanostructures. The enhanced electrocatalytic activity, excitation wavelength dependent down-conversion and up-conversion photoluminescence, growth of structural polymorphs using simple hydrothermal method, and the efficient anticancer properties of MoS2 nanostructures providing greater insight into energy and biomedical applications are also discussed. The improved catalytic activity of MoS2-based nanostructures reveals the increasing number of accessible active sites, formation of large surface area and is greatly beneficial for accomplishing a clean, environmental-friendly, inexpensive hydrogen mission for energy storage and conversion applications. The synergistic effect of the MoS2 nanocomposites was able to impede angiogenesis, tumor growth, and epithelial to mesenchymal transition, elucidating the anticancer efficacy. Understanding and exploiting such unique properties of these 2D materials paves new horizons toward novel technological advances in electronic and medical field.

MoS2, a new perspective beyond graphene

One of the essential systems in autonomous vehicles for ensuring a secure circumstance for drivers and passengers is the Advanced Driver Assistance System (ADAS). Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection are examples of ADAS. Lane detection displays information specific to the geometrical features of lane line structures to the vehicle’s intelligent system to show the position of lane markings. This article reviews the methods employed for lane detection in an autonomous vehicle. A systematic literature review (SLR) has been carried out to analyze the most delicate approach to detecting the road lane for the benefit of the automation industry. One hundred and two publications from well-known databases were chosen for this review. The trend was discovered after thoroughly examining the selected articles on the method implemented for detecting the road lane from 2018 until 2021. The selected literature used various methods, with the input dataset being one of two types: self-collected or acquired from an online public dataset. In the meantime, the methodologies include geometric modeling and traditional methods, while AI includes deep learning and machine learning. The use of deep learning has been increasingly researched throughout the last four years. Some studies used stand-alone deep learning implementations for lane detection problems. Meanwhile, some research focuses on merging deep learning with other machine learning techniques and classical methodologies. Recent advancements imply that attention mechanism has become a popular combined strategy with deep learning methods. The use of deep algorithms in conjunction with other techniques showed promising outcomes. This research aims to provide a complete overview of the literature on lane detection methods, highlighting which approaches are currently being researched and the performance of existing state-of-the-art techniques. Also, the paper covered the equipment used to collect the dataset for the training process and the dataset used for network training, validation, and testing. This review yields a valuable foundation on lane detection techniques, challenges, and opportunities and supports new research works in this automation field. For further study, it is suggested to put more effort into accuracy improvement, increased speed performance, and more challenging work on various extreme conditions in detecting the road lane.

Lane Detection in Autonomous Vehicles: A Systematic Review

The data collected by floating cars is an important source for lane-level map production. Compared with other data sources, this method is a low-cost but challenging way to generate high-accuracy maps. In this paper, we propose a data correction algorithm for low-frequency floating car data. First, we preprocess the trajectory data by an adaptive density optimizing method to remove the noise points with large mistakes. Then, we match the trajectory data with OpenStreetMap (OSM) using an efficient hierarchical map matching algorithm. Lastly, we correct the floating car data by an OSM-based physical attraction model. Experiments are conducted exploiting the data collected by thousands of taxies over one week in Wuhan City, China. The results show that the accuracy of the data is improved and the proposed algorithm is demonstrated to be practical and effective.

A Data Correction Algorithm for Low-Frequency Floating Car Data.

Lane detection algorithm based on monocular camera is one of the most popular methods in recent years, which can meet the requirement of real-time and robust for autonomous vehicle. In this way, the position of lane markers can be transferred from perspective space to road space base on the planar road assumption. However, large numbers of road scenes, especially the up and down slope road environment, cannot meet this requirement.In this paper, we propose a multiple vanishing point detection method to reconstruct the road space in slope scenes. In order to improve the accuracy of vanishing point estimation, the road images are decomposed into near and far regions. We extract candidate lane markers in near region by using multiscale convolution kernel and Hough Transform at first. Then, the lane markers in far region can be detected based on the result of near region. At last, different vanishing points are extracted in near and far regions. With the help of a vanishing point based on camera model, we can project both of near and far regions into road space. The experiment is conducted on our self-driving car ‘TuLian’ in campus environment.

Yi Cai

Papers

A Data Correction Algorithm for Low-Frequency Floating Car Data.

Lane Detection and Road Surface Reconstruction Based on Multiple Vanishing Point & Symposia