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

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.

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

Sensors ◽

10.3390/s18113928 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3928 ◽

Cited By ~ 9

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.

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Road-Aware Trajectory Prediction for Autonomous Driving on Highways

Sensors ◽

10.3390/s20174703 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4703

Author(s):

Yookhyun Yoon ◽

Taeyeon Kim ◽

Ho Lee ◽

Jahnghyon Park

Keyword(s):

Deep Learning ◽

Autonomous Vehicles ◽

Prediction Method ◽

Autonomous Driving ◽

Structural Constraints ◽

High Definition ◽

Trajectory Prediction ◽

The Road ◽

Road Geometry ◽

Efficient Learning

For driving safely and comfortably, the long-term trajectory prediction of surrounding vehicles is essential for autonomous vehicles. For handling the uncertain nature of trajectory prediction, deep-learning-based approaches have been proposed previously. An on-road vehicle must obey road geometry, i.e., it should run within the constraint of the road shape. Herein, we present a novel road-aware trajectory prediction method which leverages the use of high-definition maps with a deep learning network. We developed a data-efficient learning framework for the trajectory prediction network in the curvilinear coordinate system of the road and a lane assignment for the surrounding vehicles. Then, we proposed a novel output-constrained sequence-to-sequence trajectory prediction network to incorporate the structural constraints of the road. Our method uses these structural constraints as prior knowledge for the prediction network. It is not only used as an input to the trajectory prediction network, but is also included in the constrained loss function of the maneuver recognition network. Accordingly, the proposed method can predict a feasible and realistic intention of the driver and trajectory. Our method has been evaluated using a real traffic dataset, and the results thus obtained show that it is data-efficient and can predict reasonable trajectories at merging sections.

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A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR

Electronics ◽

10.3390/electronics8010043 ◽

2019 ◽

Vol 8 (1) ◽

pp. 43 ◽

Cited By ~ 6

Author(s):

Rendong Wang ◽

Youchun Xu ◽

Miguel Angel Sotelo ◽

Yulin Ma ◽

Thompson Sarkodie-Gyan ◽

...

Keyword(s):

Dynamic Environment ◽

Point Clouds ◽

Autonomous Driving ◽

Initial Position ◽

Urban Environments ◽

Data Set ◽

Registration Method ◽

Position Errors ◽

Initial Errors ◽

High Dynamic

The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively.

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Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2015.p0660 ◽

2015 ◽

Vol 27 (6) ◽

pp. 660-670 ◽

Cited By ~ 6

Author(s):

Udara Eshan Manawadu ◽

◽

Masaaki Ishikawa ◽

Mitsuhiro Kamezaki ◽

Shigeki Sugano ◽

...

Keyword(s):

Autonomous Vehicles ◽

Driving Simulator ◽

Autonomous Driving ◽

Driving Experience ◽

The Road ◽

Traffic Conditions ◽

Passenger Vehicles ◽

New Type ◽

On The Road ◽

Near Future

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.

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Implementation of a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments

Sensors ◽

10.3390/s19153318 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3318 ◽

Cited By ~ 2

Author(s):

Carlos Martínez ◽

Felipe Jiménez

Keyword(s):

Autonomous Vehicles ◽

Vision System ◽

Autonomous Driving ◽

Road User ◽

Measurement Unit ◽

Maximum Speed ◽

The Road ◽

Planning Algorithm ◽

High Level ◽

Path Planning Algorithm

Autonomous driving is undergoing huge developments nowadays. It is expected that its implementation will bring many benefits. Autonomous cars must deal with tasks at different levels. Although some of them are currently solved, and perception systems provide quite an accurate and complete description of the environment, high-level decisions are hard to obtain in challenging scenarios. Moreover, they must comply with safety, reliability and predictability requirements, road user acceptance, and comfort specifications. This paper presents a path planning algorithm based on potential fields. Potential models are adjusted so that their behavior is appropriate to the environment and the dynamics of the vehicle and they can face almost any unexpected scenarios. The response of the system considers the road characteristics (e.g., maximum speed, lane line curvature, etc.) and the presence of obstacles and other users. The algorithm has been tested on an automated vehicle equipped with a GPS receiver, an inertial measurement unit and a computer vision system in real environments with satisfactory results.

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MEMS Mirrors for LiDAR: A Review

Micromachines ◽

10.3390/mi11050456 ◽

2020 ◽

Vol 11 (5) ◽

pp. 456 ◽

Cited By ~ 3

Author(s):

Dingkang Wang ◽

Connor Watkins ◽

Huikai Xie

Keyword(s):

Autonomous Vehicles ◽

Figure Of Merit ◽

Three Dimensional ◽

Point Clouds ◽

Autonomous Driving ◽

Field Of View ◽

Preliminary Assessment ◽

Wide Range ◽

Laser Scanners ◽

Increasing Demand

In recent years, Light Detection and Ranging (LiDAR) has been drawing extensive attention both in academia and industry because of the increasing demand for autonomous vehicles. LiDAR is believed to be the crucial sensor for autonomous driving and flying, as it can provide high-density point clouds with accurate three-dimensional information. This review presents an extensive overview of Microelectronechanical Systems (MEMS) scanning mirrors specifically for applications in LiDAR systems. MEMS mirror-based laser scanners have unrivalled advantages in terms of size, speed and cost over other types of laser scanners, making them ideal for LiDAR in a wide range of applications. A figure of merit (FoM) is defined for MEMS mirrors in LiDAR scanners in terms of aperture size, field of view (FoV) and resonant frequency. Various MEMS mirrors based on different actuation mechanisms are compared using the FoM. Finally, a preliminary assessment of off-the-shelf MEMS scanned LiDAR systems is given.

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Real-Time Mine Road Boundary Detection and Tracking for Autonomous Truck

Sensors ◽

10.3390/s20041121 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1121

Author(s):

Xiaowei Lu ◽

Yunfeng Ai ◽

Bin Tian

Keyword(s):

Real Time ◽

Point Clouds ◽

Autonomous Driving ◽

Boundary Detection ◽

Spatial Distance ◽

Boundary Line ◽

The Road ◽

Detection And Tracking ◽

Ground Point ◽

Road Data

Road boundary detection is an important part of the perception of the autonomous driving. It is difficult to detect road boundaries of unstructured roads because there are no curbs. There are no clear boundaries on mine roads to distinguish areas within the road boundary line and areas outside the road boundary line. This paper proposes a real-time road boundary detection and tracking method by a 3D-LIDAR sensor. The road boundary points are extracted from the detected elevated point clouds above the ground point cloud according to the spatial distance characteristics and the angular features. Road tracking is to predict and update the boundary point information in real-time, in order to prevent false and missed detection. The experimental verification of mine road data shows the accuracy and robustness of the proposed algorithm.

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Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

Sensors ◽

10.3390/s19214711 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4711 ◽

Cited By ~ 2

Author(s):

Kewei Wang ◽

Fuwu Yan ◽

Bin Zou ◽

Luqi Tang ◽

Quan Yuan ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Autonomous Vehicles ◽

Autonomous Driving ◽

Dynamic Scenes ◽

Comprehensive Understanding ◽

Global Context ◽

The Road ◽

Semantic Domain ◽

Road Segmentation

The deep convolutional neural network has led the trend of vision-based road detection, however, obtaining a full road area despite the occlusion from monocular vision remains challenging due to the dynamic scenes in autonomous driving. Inferring the occluded road area requires a comprehensive understanding of the geometry and the semantics of the visible scene. To this end, we create a small but effective dataset based on the KITTI dataset named KITTI-OFRS (KITTI-occlusion-free road segmentation) dataset and propose a lightweight and efficient, fully convolutional neural network called OFRSNet (occlusion-free road segmentation network) that learns to predict occluded portions of the road in the semantic domain by looking around foreground objects and visible road layout. In particular, the global context module is used to build up the down-sampling and joint context up-sampling block in our network, which promotes the performance of the network. Moreover, a spatially-weighted cross-entropy loss is designed to significantly increases the accuracy of this task. Extensive experiments on different datasets verify the effectiveness of the proposed approach, and comparisons with current excellent methods show that the proposed method outperforms the baseline models by obtaining a better trade-off between accuracy and runtime, which makes our approach is able to be applied to autonomous vehicles in real-time.

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Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving

Sensors ◽

10.3390/s20205900 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5900

Author(s):

Sungjin Cho ◽

Chansoo Kim ◽

Jaehyun Park ◽

Myoungho Sunwoo ◽

Kichun Jo

Keyword(s):

Autonomous Vehicles ◽

Semantic Information ◽

Semantic Segmentation ◽

Point Clouds ◽

Autonomous Driving ◽

Map Building ◽

Probability Models ◽

Semantic Class ◽

Probabilistic Uncertainty ◽

Segmentation Algorithms

LiDAR-based Simultaneous Localization And Mapping (SLAM), which provides environmental information for autonomous vehicles by map building, is a major challenge for autonomous driving. In addition, the semantic information has been used for the LiDAR-based SLAM with the advent of deep neural network-based semantic segmentation algorithms. The semantic segmented point clouds provide a much greater range of functionality for autonomous vehicles than geometry alone, which can play an important role in the mapping step. However, due to the uncertainty of the semantic segmentation algorithms, the semantic segmented point clouds have limitations in being directly used for SLAM. In order to solve the limitations, this paper proposes a semantic segmentation-based LiDAR SLAM system considering the uncertainty of the semantic segmentation algorithms. The uncertainty is explicitly modeled by proposed probability models which are come from the data-driven approaches. Based on the probability models, this paper proposes semantic registration which calculates the transformation relationship of consecutive point clouds using semantic information with proposed probability models. Furthermore, the proposed probability models are used to determine the semantic class of the points when the multiple scans indicate different classes due to the uncertainty. The proposed framework is verified and evaluated by the KITTI dataset and outdoor environments. The experiment results show that the proposed semantic mapping framework reduces the errors of the mapping poses and eliminates the ambiguity of the semantic information of the generated semantic map.

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5G-Enabled Autonomous Driving Demonstration with a V2X Scenario-in-the-Loop Approach

Sensors ◽

10.3390/s20247344 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7344

Author(s):

Zsolt Szalay ◽

Dániel Ficzere ◽

Viktor Tihanyi ◽

Ferenc Magyar ◽

Gábor Soós ◽

...

Keyword(s):

Autonomous Vehicles ◽

Mass Communication ◽

Real Life ◽

Autonomous Driving ◽

Mobile Technologies ◽

Low Latency ◽

Vehicular Communication ◽

Digital Twin ◽

The Road ◽

On The Road

Autonomous vehicles are at the forefront of interest due to the expectations of changing transportation for the better. In order to make better decisions on the road, vehicles use information from various sources: their own sensors, messages arriving from surrounding vehicles and objects, as well as from centralized entities—including their own Digital Twin. Certain decisions require the information to arrive with low latency and some of this information (such as video) requires broadband communication. Furthermore, the vehicles can populate an area, so they can represent mass communication endpoints that still need low latency and massive broadband. The mobility of the vehicles obviously requires the complete coverage of the roads with reliable wireless communication technologies fulfilling the previously mentioned needs. The fifth generation of cellular mobile technologies, 5G, addresses these requirements. The current paper presents real-life scenarios—on the M86 highway and the ZalaZONE proving ground in Hungary—for the demonstration of vehicular communication with 5G support, where the cars exchange sensor and control information with each other, their environment, and their Digital Twins. The demonstrations were carried out through the Scenario-in-the-Loop (SciL) methodology, where some of the actionable triggers were not physically present around the vehicles, but sensed or simulated around their Digital Twin. The measurements around the demonstrations aim to reveal the feasibility of the 5G Non-Standalone Architecture for certain communication scenarios, and they mainly aim to reveal the current latency and throughput limitations under real-life conditions.

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