Development of vehicle maneuvering system for autonomous driving

Choi, Jung Hyun; Lee, Hyukjin; Oh, Sehoon; Nam, Kwangwoo

doi:10.1016/j.mechatronics.2022.102798

Cited by 9 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overall driverless vehicle framework includes an environment perception system [ 11 ] to recognize the surrounding environment, a planning decision tree system [ 12 ] to plan the driving route, and a motion control system [ 13 ] to control the behavior of the vehicle. Fig 1 presents the specific architecture.…”

Section: Methodsmentioning

confidence: 99%

“…The overall driverless vehicle framework includes an environment perception system [11] to recognize the surrounding environment, a planning decision tree system [12] to plan the driving route, and a motion control system [13] to control the behavior of the vehicle. Autonomous vehicles depend on positioning and navigation technology [14], information fusion technology [15], and wire-based transformation technology [16].…”

Section: Basic Framework and Technical Support For Driverless Carsmentioning

confidence: 99%

See 1 more Smart Citation

Exploration of the intelligent control system of autonomous vehicles based on edge computing

Ming

2023

PLoS ONE

View full text Add to dashboard Cite

The development of science and technology continues to promote the progress of society. The current intelligence and automation technology has become widely used in society. To this end, this study proposes a vehicle intelligent control system based on edge computing and deep learning to promote the far-reaching development of intelligent technology and automation technology. First, control algorithms are used to design a switch control strategy combining accelerator and brake. Second, a fuzzy control algorithm based on vehicle tracking and trajectory deviation is designed to enhance the vehicle’s stability during steering. A Convolutional Neural Network (CNN) is used to recognize the car’s surroundings as it drives. In addition, accelerator and brake controllers and vehicle tracking and trajectory deviation controllers are connected to the vehicle’s wiring. Then, the data transmission function based on edge computing is applied to the vehicle’s intelligent control system. Finally, trajectory tracking and emergency braking experiments are carried out on the control system to verify the practicability and reliability of the method and the effectiveness of CNN. The simulation experiments are carried out on two states of medium speed and high speed to verify the effectiveness of the longitudinal anti-collision system of the test vehicle when the target vehicle suddenly decelerates. The results demonstrate that the driving speed of the experimental vehicle is set to 50km/h, the distance between the experimental vehicle and the target vehicle is 40m, and the target vehicle in front drives at a constant speed of 50km/h. The target vehicle in front of the car suddenly decelerates in 5 seconds, and the speed drops to 0 after 5 seconds. The actual distance between the experimental vehicle and the target vehicle is very close to the expected safe space, and the experimental vehicle is in a safe state during this process. When the experimental vehicle starts to decelerate, the experimental vehicle adopts emergency deceleration to ensure a safe distance between the two vehicles. At this time, the car enters the second-level early warning state, but driving safety can still be guaranteed. It is advisable to maintain low-speed emergency braking in this state. This study provides creative research ideas for the follow-up research on the intelligent control system of uncrewed vehicles and contributes to the development of intelligence and automation technology.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The overall driverless vehicle framework includes an environment perception system [11] to recognize the surrounding environment, a planning decision tree system [12] to plan the driving route, and a motion control system [13] to control the behavior of the vehicle. Autonomous vehicles depend on positioning and navigation technology [14], information fusion technology [15], and wire-based transformation technology [16].…”

Section: Basic Framework and Technical Support For Driverless Carsmentioning

confidence: 99%

Exploration of the intelligent control system of autonomous vehicles based on edge computing

Ming

2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In the ever-evolving landscape of the automotive industry, it is undeniable that intelligence, connectivity, and automation stand as indisputable hallmarks of the future [1]. The continuous advancements in intelligent networking and autonomous vehicle technologies underscore the paramount importance of the "Software Defined Vehicle" (SDV) concept [2]. Software has undeniably emerged as the primary catalyst propelling innovation in automotive technology, steering the course of product differentiation and evolving into the very core of automotive informatization and smartification.…”

Section: Introductionmentioning

confidence: 99%

Intelligent connected vehicle health monitoring system based on pulse diagnosis technology

Zhang,

Lin,

Zhang

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

View full text Add to dashboard Cite

In 2022, the National Bureau of Statistics reported a distressing toll of 61,703 lives lost and 250,723 injuries due to traffic accidents nationwide. This equates to a tragic average of one life lost every seven minutes, propelling our country to the top of global traffic accident statistics. Beyond the well-known factors contributing to accidents like fatigue and reckless behavior, there has been a concerning surge in accidents linked to underlying health conditions, notably heart disease and high blood pressure from poor health. In response, we've developed an innovative in-vehicle human health monitoring system. This product seamlessly integrates a pulse diagnosis module into the steering wheel, allowing real-time monitoring of pulse waveforms and critical health data, a pivotal component of our design. Our system also utilizes a trained opensource model to develop modules for monitoring and providing reminders for various driver behaviors, including closedeye detection and phone call management. By meticulously analyzing the connections between the driver's heart condition, pulse diagnostics, and vehicle speed, our product generates personalized driving speed and timing recommendations that prioritize the driver's well-being. Furthermore, in emergencies, our system promptly notifies the driver's family of any detected physical abnormalities or the driver's whereabouts, instilling peace of mind during journeys. In essence, our product proactively mitigates the risks associated with traffic accidents stemming from driver fatigue, reckless driving practices, or irregular pulse patterns. It promotes safer driving habits and contributes to the early detection of certain health conditions, fostering safer roads and improved health outcomes.

show abstract

“…In practice, data-driven approaches are often more feasible, flexible, and accurate compared to physical model-based approaches [ 19 , 20 ]. In recent years, artificial intelligence (AI) [ 21 ] has gained wide usage in various fields, including autonomous driving [ 22 ] and robot manipulator dynamics [ 23 ]. AI-based techniques are primarily data-driven, as they analyze existing observational data and establish input–output mapping relationships to predict specific building energy data.…”

Section: Introductionmentioning

confidence: 99%

Study on the Impact of Building Energy Predictions Considering Weather Errors of Neighboring Weather Stations

Li,

Wang,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

Weather data errors affect energy management by influencing the accuracy of building energy predictions. This study presents a long short-term memory (LSTM) prediction model based on the “Energy Detective” dataset (Shanghai, China) and neighboring weather station data. The study analyzes the errors of different weather data sources (Detective and A) at the same latitude and longitude. Subsequently, it discusses the effects of weather errors from neighboring weather stations (Detective, A, B, C, and D) on energy forecasts for the next hour and day including the selection process for neighboring weather stations. Furthermore, it compares the forecast results for summer and autumn. The findings indicate a correlation between weather errors from neighboring weather stations and energy consumption. The median R-Square for predicting the next hour reached 0.95. The model’s predictions for the next day exhibit a higher Prediction Interval Mean Width (139.0 in summer and 146.1 in autumn), indicating a greater uncertainty.

show abstract

Development of vehicle maneuvering system for autonomous driving

Cited by 9 publications

References 14 publications

Exploration of the intelligent control system of autonomous vehicles based on edge computing

Exploration of the intelligent control system of autonomous vehicles based on edge computing

Intelligent connected vehicle health monitoring system based on pulse diagnosis technology

Study on the Impact of Building Energy Predictions Considering Weather Errors of Neighboring Weather Stations

Contact Info

Product

Resources

About