Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Chan, Teck Kai; Chin, Cheng Siong

doi:10.3390/electronics10091021

Cited by 24 publications

(9 citation statements)

References 138 publications

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“…In location prediction, the Markovian [20] model has limitations. In the case of aircraft, there is also a method of predicting the position of aircraft through the aircraft trajectory [21] dictionary file data based on flight histories. The trend of current location prediction is recognized as the most important value for obtaining a future prediction value for a location visited by a user.…”

Section: Related Workmentioning

confidence: 99%

Low Power Sensor Location Prediction Using Spatial Dimension Transformation and Pattern Recognition

Lee

Jeong

2022

Energies

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A method of positioning a location on a specific object using a wireless sensor has been developed for a long time. However, due to the error of wavelengths and various interference factors occurring in three-dimensional space, accurate positioning is difficult, and predicting future locations is even more difficult. It uses IoT-based node pattern recognition technology to overcome positioning errors or inaccurate predictions in wireless sensor networks. It developed a method to improve the current positioning accuracy in a sensor network environment and a method to learn a pattern of position data directly from a wavelength receiver. The developed method consists of two steps: The first step is a method of changing location data in 3D space to location data in 2D space in order to reduce the possibility of positioning errors in 3D space. The second step is to reduce the range of the moving direction angle in which the data changed in two dimensions can be changed in the future and to predict future positions through pattern recognition of the position data. It is to calculate the expected position in the future. In conclusion, three-dimensional positioning accuracy was improved through this method, and future positioning accuracy was also improved. The core technology was able to reduce inevitable errors by changing the spatial dimension from 3D to 2D and to improve the accuracy of future location prediction by reducing the range of the movable direction angle of the location data changed to 2D. It was also possible to obtain the result that the prediction accuracy increases in proportion to the amount of data accumulated in the wavelength receiver and the learning time. In the era of the Fourth Industrial Revolution, this method is expected to be utilized in various places, such as smart cities, autonomous vehicles, and disaster prediction.

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Section: Related Workmentioning

confidence: 99%

Low Power Sensor Location Prediction Using Spatial Dimension Transformation and Pattern Recognition

Lee

Jeong

2022

Energies

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“…So far, most current research related to ADS safety has focused on essential driverless functions, such as lane departure suppression, collision prevention between forwarding and lane changes, autonomous cruise driving, and driverless parking. Other related studies have focused on driving safety concerns under normal and abnormal conditions, such as driving in bad weather or special road conditions, sudden obstacles, hazard avoidance, blind-spot monitoring, fatigued driving prevention, and abnormal behavior of other road users [3][4][5][6][7]. However, for the public, the results of a fully government-implemented ADS safety test would be more impartial and credible than individual announcements by ADS developers or media reports that may be subjective and misleading.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment and Enhancement Suggestions for Automated Driving Systems through Examining Testing Collision and Disengagement Reports

Liao

et al. 2023

Journal of Advanced Transportation

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The California Department of Motor Vehicles (DMV) reports, including disengagement and collision reports, provide information on each accident or disengagement activity for on-road testing of autonomous driving systems (ADSs) and autonomous vehicles (AVs). Unfortunately, current DMV reports have been misleading in relation to many key details, making it challenging for readers of those reports to discern the events’ root causes and interrelationships. Therefore, appropriate systematic classification methods and principles need to be adopted. We follow an identification method similar to fault tree analysis (FTA) with the help of the driving reliability and error analysis method (DREAM 3.0) and the Haddon matrix to find the potential key accident factors from all disengagement data. We also conduct ADS risk assessments of potential disengagements and genuine accidents classified by traditional accident types. In addition, the automated driving system is composed of various software modules, and a classification method that is suitable from the standpoint of ADS software developers is developed in this paper. Next, we sort out the characteristics of the most frequent accidents based on the risk assessment results. Finally, we propose a workable risk reduction solution according to the characteristics of accidents.

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“…Not only are there "urban canyons" that limit Line-Of-Sight and create environments that allow multiple signals from the same satellite to arrive at a GNSS receiver, but also present challenges in increased interference from other radio systems [1]. This is especially true for autonomous vehicles where full self-control is desired for smarter cities [2]. These cities could deploy autonomous air and ground vehicles for various tasks including critical missions such as disasters and pandemics.…”

Section: Introductionmentioning

confidence: 99%

An INS/GNSS fusion architecture in GNSS denied environment using gated recurrent unit

Geragersian

Petrunin

Guo

et al. 2022

AIAA SCITECH 2022 Forum

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One of the most used Position, Navigation and Timing (PNT) technology of the 21st century is Global Navigation Satellite Systems (GNSS). GNSS signals are affected by urban canyons that limit line-of-sight and reduce satellite availability to receivers. Smart cities are expected to adopt autonomous Unmanned Aerial Vehicles (UAV) operations for critical missions such as transportation of organs which are time-sensitive. Therefore, higher accuracy for position and velocity information is required. This paper investigates the use of Gated Recurrent Units (GRU) as a suitable technique that can memorize previous information in conjunction with the inputs (consisting of attitude, change in attitude, and change in velocity) to reduce position and velocity error when GNSS is not available. The fusion approach is developed and tested using Spirent's SimGEN GSS7000 hardware simulator which simulates GNSS signals and Spirent's SimSENSOR software that simulates accelerometer and gyroscope stochastic and deterministic errors. GNSS outage is varied between 1 and 20 seconds randomly to affect predicted position and velocity. The data is collected and used to train the GRU to predict the position and velocity error measured by the Inertial Measurement Unit (IMU). From the performance evaluation, a 60% reduction in Root Mean Squared Error (RMSE) is observed compared to Recurrent Neural Networks (RNN). Comparing 95 th percentile with Inertial Navigation System (INS), RNN, and GRU, an 80% reduction is observed between INS and RNN. Furthermore, a 35% drop in the 95 th percentile is observed between RNN and GRU.

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Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Cited by 24 publications

References 138 publications

Low Power Sensor Location Prediction Using Spatial Dimension Transformation and Pattern Recognition

Low Power Sensor Location Prediction Using Spatial Dimension Transformation and Pattern Recognition

Risk Assessment and Enhancement Suggestions for Automated Driving Systems through Examining Testing Collision and Disengagement Reports

An INS/GNSS fusion architecture in GNSS denied environment using gated recurrent unit

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