scholarly journals Performance Analysis of NDT-based Graph SLAM for Autonomous Vehicle in Diverse Typical Driving Scenarios of Hong Kong

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3928 ◽  
Author(s):  
Weisong Wen ◽  
Li-Ta Hsu ◽  
Guohao Zhang
Keyword(s):  
New York ◽  
Hong Kong ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Tall Buildings ◽  
Point Clouds ◽  
Reliability Estimation ◽  
Traffic Condition ◽  
Localization And Mapping ◽  
Graph Slam

Robust and lane-level positioning is essential for autonomous vehicles. As an irreplaceable sensor, Light detection and ranging (LiDAR) can provide continuous and high-frequency pose estimation by means of mapping, on condition that enough environment features are available. The error of mapping can accumulate over time. Therefore, LiDAR is usually integrated with other sensors. In diverse urban scenarios, the environment feature availability relies heavily on the traffic (moving and static objects) and the degree of urbanization. Common LiDAR-based simultaneous localization and mapping (SLAM) demonstrations tend to be studied in light traffic and less urbanized area. However, its performance can be severely challenged in deep urbanized cities, such as Hong Kong, Tokyo, and New York with dense traffic and tall buildings. This paper proposes to analyze the performance of standalone NDT-based graph SLAM and its reliability estimation in diverse urban scenarios to further evaluate the relationship between the performance of LiDAR-based SLAM and scenario conditions. The normal distribution transform (NDT) is employed to calculate the transformation between frames of point clouds. Then, the LiDAR odometry is performed based on the calculated continuous transformation. The state-of-the-art graph-based optimization is used to integrate the LiDAR odometry measurements to implement optimization. The 3D building models are generated and the definition of the degree of urbanization based on Skyplot is proposed. Experiments are implemented in different scenarios with different degrees of urbanization and traffic conditions. The results show that the performance of the LiDAR-based SLAM using NDT is strongly related to the traffic condition and degree of urbanization. The best performance is achieved in the sparse area with normal traffic and the worse performance is obtained in dense urban area with 3D positioning error (summation of horizontal and vertical) gradients of 0.024 m/s and 0.189 m/s, respectively. The analyzed results can be a comprehensive benchmark for evaluating the performance of standalone NDT-based graph SLAM in diverse scenarios which is significant for multi-sensor fusion of autonomous vehicle.

scholarly journals Novel Routing Algorithm for Autonomous Vehicles in Smart Transportation System

2021 ◽  
Vol 3 (3) ◽  
pp. 164-179
Author(s):  
Haoxiang Wang
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Autonomous Vehicle ◽  
Routing Algorithm ◽  
Transportation System ◽  
Traffic Condition ◽  
Smart Transportation ◽  
Centralized Management ◽  
The City

In recent times Automation is emerging every day and bloomed in every sector. Intelligent Transportation System (ITS) is one of the important branches of Automation. The major constrain in the transportation system is traffic congestion. This slurps the individual’s time and consequently pollutes the environment. A centralized management is required for optimizing the transportation system. The current traffic condition is predicted by evaluating the historical data and thereby it reduces the traffic congestion. The periodic update of traffic condition in each and every street of the city is obtained and the data is transferred to the autonomous vehicle. These data are obtained from the simulation results of transportation prediction tool SUMO. It is proved that our proposed work reduces the traffic congestion and maintains ease traffic flow and preserves the fleet management.

scholarly journals Design and Implementation of Edge-Fog-Cloud System through HD Map Generation from LiDAR Data of Autonomous Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2084
Author(s):  
Junwon Lee ◽  
Kieun Lee ◽  
Aelee Yoo ◽  
Changjoo Moon
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Fog Computing ◽  
Point Clouds ◽  
Lidar Data ◽  
High Definition ◽  
Localization And Mapping ◽  
Cloud Server ◽  
Map Generation ◽  
Self Driving Cars

Self-driving cars, autonomous vehicles (AVs), and connected cars combine the Internet of Things (IoT) and automobile technologies, thus contributing to the development of society. However, processing the big data generated by AVs is a challenge due to overloading issues. Additionally, near real-time/real-time IoT services play a significant role in vehicle safety. Therefore, the architecture of an IoT system that collects and processes data, and provides services for vehicle driving, is an important consideration. In this study, we propose a fog computing server model that generates a high-definition (HD) map using light detection and ranging (LiDAR) data generated from an AV. The driving vehicle edge node transmits the LiDAR point cloud information to the fog server through a wireless network. The fog server generates an HD map by applying the Normal Distribution Transform-Simultaneous Localization and Mapping(NDT-SLAM) algorithm to the point clouds transmitted from the multiple edge nodes. Subsequently, the coordinate information of the HD map generated in the sensor frame is converted to the coordinate information of the global frame and transmitted to the cloud server. Then, the cloud server creates an HD map by integrating the collected point clouds using coordinate information.

scholarly journals An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach

2013 ◽  
Vol 23 (1) ◽  
pp. 183-200 ◽  
Author(s):  
Fei Yan ◽  
Mahjoub Dridi ◽  
Abdellah El Moudni
Keyword(s):  
Genetic Algorithm ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Control Methods ◽  
Traffic Condition ◽  
Sequencing Problem ◽  
Genetic Algorithm Approach ◽  
Short Time ◽  
Intersection Management

This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.

Comparison of Light Rail Streetcar Against Shared Autonomous Vehicle Fleet for Brooklyn–Queens Connector in New York City

2017 ◽  
Vol 2650 (1) ◽  
pp. 142-151 ◽  
Author(s):  
Lucas Mestres Mendes ◽  
Manel Rivera Bennàssar ◽  
Joseph Y. J. Chow
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Light Rail ◽  
Rail System ◽  
Total Travel Time ◽  
Vehicle Fleet ◽  
Event Based

Policy makers predict that autonomous vehicles will have significant market penetration in the next decade or so. In several simulation studies shared autonomous vehicle fleets have been shown to be effective public transit alternatives. This study compared the effectiveness of a shared autonomous vehicle fleet with an upcoming transit project proposed in New York City, the Brooklyn–Queens Connector light rail project. The study developed an event-based simulation model to compare the performance of the shared autonomous vehicle system with the light rail system under the same demand patterns, route alignment, and operating speeds. The simulation experiments revealed that a shared autonomous vehicle fleet of 500 vehicles of 12-person capacity (consistent with the EZ10 vehicle) would be needed to match the 39-vehicle light rail system if operated as a fixed-route system. However, as a demand-responsive system, a fleet of only 150 vehicles would lead to the same total travel time (22 min) as the 39-vehicle fleet light rail system. Furthermore, a fleet of 450 12-person vehicles in a demand-responsive operation would have the same average wait times while reducing total travel times by 36%. The implications for system resiliency, idle vehicle allocation, and vehicle modularity are discussed.

scholarly journals A Brief Survey on SLAM Methods in Autonomous Vehicle

2018 ◽  
Vol 7 (4.27) ◽  
pp. 38 ◽  
Author(s):  
Talha Takleh Omar Takleh ◽  
Nordin Abu Bakar ◽  
Shuzlina Abdul Rahman ◽  
Raseeda Hamzah ◽  
Zalilah Abd Aziz
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Simultaneous Localization And Mapping ◽  
General Concept ◽  
Research Community ◽  
Current Trends ◽  
Localization And Mapping ◽  
Pros And Cons ◽  
Self Driving Cars

The overall purpose of this paper is to provide an introductory survey in the area of Simultaneous Localization and Mapping (SLAM) particularly its utilization in autonomous vehicle or more specifically in self-driving cars, especially after the release of commercial semi-autonomous car like the Tesla vehicles as well as the Google Waymo vehicle. Before we begin diving into the concept of SLAM, we need to understand the importance of SLAM and problems that expand to the various methods developed by numerous researchers to solve it. Thus, in this paper we will start by giving the general concept behind SLAM, followed by sharing details of its different categories and the various methods that form the SLAM function in today’s autonomous vehicles; which can solve the SLAM problem. These methods are the current trends that are widely focused in the research community in producing solutions to the SLAM problem; not only in autonomous vehicle but in the robotics field as well. Next, we will compare each of these methods in terms of its pros and cons before concluding the paper by looking at future SLAM challenges. 

scholarly journals POU-SLAM: Scan-to-Model Matching Based on 3D Voxels

2019 ◽  
Author(s):  
Jianwen Jiang ◽  
Jikai Wang ◽  
Peng Wang ◽  
Zonghai Chen
Keyword(s):  
Visual Information ◽  
Autonomous Vehicles ◽  
Partition Of Unity ◽  
Point Clouds ◽  
Model Matching ◽  
Implicit Surface ◽  
Fundamental Building Block ◽  
Environment Model ◽  
Localization And Mapping ◽  
Solution Design

Purpose: Localization and mapping with LiDAR data is a fundamental building block for autonomous vehicles. Though LiDAR point clouds can often encode the scene depth more accurate and steadier compared with visual information, laser-based Simultaneous Localization And Mapping (SLAM) remains challengeable as the data is usually sparse, density variable and less discriminative. The purpose of this paper is to propose an accurate and reliable laser-based SLAM solution. Design/methodology/approach: The method starts with constructing voxel grids based on the 3D input point cloud. These voxels are then classified into three types to indicate different physical objects according to the spatial distribution of the points contained in each voxel. A global environment model with Partition of Unity (POU) implicit surface is maintained along the process and located voxels are merged into it from stage to stage, through scan-to-model matching implemented by Levenberg-Marquardt method. Findings: We find a laser-based SLAM method. The method uses POU implicit surface representation to build the model and is evaluated on the KITTI odometry benchmark without loop closure. Experimental results indicate that the method achieves accuracy comparable to the state-of-the-art methods. Originality/value: We propose a novel, low-drift SLAM method, which falls into a scan-to-model matching paradigm, operates on point clouds obtained from Velodyne HDL64. The method is of value to researchers developing SLAM systems for autonomous vehicles.

scholarly journals Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR

2019 ◽  
Vol 11 (14) ◽  
pp. 1726 ◽  
Author(s):  
Junqiao Zhao ◽  
Xudong He ◽  
Jun Li ◽  
Tiantian Feng ◽  
Chen Ye ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Point Clouds ◽  
Mapping Method ◽  
Structure Mapping ◽  
High Definition ◽  
The Road ◽  
Planning And Control ◽  
Localization And Mapping ◽  
Efficient Vector ◽  
And Control

The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the environment. Nevertheless, there is still a lack of SLAM method for generating vector-based road structure maps. In this paper, we propose a vector-based SLAM method for the road structure mapping using vehicle-mounted multibeam LiDAR. We propose using polylines as the primary mapping element instead of grid maps or point clouds because the vector-based representation is lightweight and precise. We explored the following: (1) the extraction and vectorization of road structures based on multiframe probabilistic fusion; (2) the efficient vector-based matching between frames of road structures; (3) the loop closure and optimization based on the pose-graph; and (4) the global reconstruction of the vector map. One specific road structure, the road boundary, is taken as an example. We applied the proposed mapping method to three road scenes, ranging from hundreds of meters to over ten kilometers and the results are automatically generated vector-based road boundary maps. The average absolute pose error of the trajectory in the mapping is 1.83 m without the aid of high-precision GPS.

scholarly journals Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles

2021 ◽  
Vol 13 (24) ◽  
pp. 5066
Author(s):  
Mohammad Aldibaja ◽  
Naoki Suganuma
Keyword(s):  
Autonomous Vehicles ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Railway Bridges ◽  
The Road ◽  
Position Errors ◽  
Challenging Environment ◽  
Graph Slam ◽  
Unique Graph ◽  
Absolute Coordinate System

This paper proposes a unique Graph SLAM framework to generate precise 2.5D LIDAR maps in an XYZ plane. A node strategy was invented to divide the road into a set of nodes. The LIDAR point clouds are smoothly accumulated in intensity and elevation images in each node. The optimization process is decomposed into applying Graph SLAM on nodes’ intensity images for eliminating the ghosting effects of the road surface in the XY plane. This step ensures true loop-closure events between nodes and precise common area estimations in the real world. Accordingly, another Graph SLAM framework was designed to bring the nodes’ elevation images into the same Z-level by making the altitudinal errors in the common areas as small as possible. A robust cost function is detailed to properly constitute the relationships between nodes and generate the map in the Absolute Coordinate System. The framework is tested against an accurate GNSS/INS-RTK system in a very challenging environment of high buildings, dense trees and longitudinal railway bridges. The experimental results verified the robustness, reliability and efficiency of the proposed framework to generate accurate 2.5D maps with eliminating the relative and global position errors in XY and Z planes. Therefore, the generated maps significantly contribute to increasing the safety of autonomous driving regardless of the road structures and environmental factors.

scholarly journals Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation

2020 ◽  
Vol 10 (23) ◽  
pp. 8534
Author(s):  
Haozhe Yang ◽  
Zhiling Wang ◽  
Linglong Lin ◽  
Huawei Liang ◽  
Weixin Huang ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Processing Time ◽  
Autonomous Vehicle ◽  
Point Clouds ◽  
Complex Objects ◽  
Time Performance ◽  
Sensor Position ◽  
Perception System

The perception system has become a topic of great importance for autonomous vehicles, as high accuracy and real-time performance can ensure safety in complex urban scenarios. Clustering is a fundamental step for parsing point cloud due to the extensive input data (over 100,000 points) of a wide variety of complex objects. It is still challenging to achieve high precision real-time performance with limited vehicle-mounted computing resources, which need to balance the accuracy and processing time. We propose a method based on a Two-Layer-Graph (TLG) structure, which can be applied in a real autonomous vehicle under urban scenarios. TLG can describe the point clouds hierarchically, we use a range graph to represent point clouds and a set graph for point cloud sets, which reduce both processing time and memory consumption. In the range graph, Euclidean distance and the angle of the sensor position with two adjacent vectors (calculated from continuing points to different direction) are used as the segmentation standard, which use the local concave features to distinguish different objects close to each other. In the set graph, we use the start and end position to express the whole set of continuous points concisely, and an improved Breadth-First-Search (BFS) algorithm is designed to update categories of point cloud sets between different channels. This method is evaluated on real vehicles and major datasets. The results show that TLG succeeds in providing a real-time performance (less than 20 ms per frame), and a high segmentation accuracy rate (93.64%) for traffic objects in the road of urban scenarios.

scholarly journals High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 899 ◽  
Author(s):  
Veli Ilci ◽  
Charles Toth
Keyword(s):  
Autonomous Vehicles ◽  
Moving Objects ◽  
Autonomous Vehicle ◽  
Point Clouds ◽  
Global Navigation Satellite Systems ◽  
Sensor Data ◽  
Measurement Unit ◽  
High Definition ◽  
Mobile Mapping ◽  
Autonomous Vehicle Navigation

Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

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