• The Journal of Korea Robotics Society
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• v.6 no.2
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• pp.97-107
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• 2011

This paper describes efficient flight control algorithms for building a reconfigurable ad-hoc wireless sensor networks between nodes on the ground and airborne nodes mounted on autonomous vehicles to increase the operational range of an aerial robot or the communication connectivity. Two autonomous flight control algorithms based on adaptive gradient climbing approach are developed to steer the aerial vehicles to reach optimal locations for the maximum communication throughputs in the airborne sensor networks. The first autonomous vehicle control algorithm is presented for seeking the source of a scalar signal by directly using the extremum-seeking based forward surge control approach with no position information of the aerial vehicle. The second flight control algorithm is developed with the angular rate command by integrating an adaptive gradient climbing technique which uses an on-line gradient estimator to identify the derivative of a performance cost function. They incorporate the network performance into the feedback path to mitigate interference and noise. A communication propagation model is used to predict the link quality of the communication connectivity between distributed nodes. Simulation study is conducted to evaluate the effectiveness of the proposed reconfigurable airborne wireless networking control algorithms.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.61-69
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• 2020

As a sales volume of autonomous vehicle continually grows up, regulations on this new technology are being introduced around the world. For example, safety standards for the Level 3 automated driving system was promulgated in December 2019 by the Ministry of Land, Infrastructure and Transport of Korean government. In order to promote the development of autonomous vehicle technology and ensure its safety simultaneously, the regulations on the automated driving systems should be phased in to keep pace with technology progress and market expansion. However, according to SAE J3016, which is well known to classify the level of the autonomous vehicle technologies, the description for classification is rather abstract. Therefore it is necessary to describe the automated driving system in more detail in terms of the 'Level.' In this study, the functions and characteristics of automated driving system are carefully classified at each level based on the commentary in the Informal Working Group (IWG) of the UN WP29. In particular, regarding the Level 4, technical issues are characterized with respect to vehicle tasks, driver tasks, system performance and regulations. The important features of the autonomous vehicles to meet Level 4 are explored on the viewpoints of driver replacement, emergency response and connected driving performance.

• Journal of Information Technology Applications and Management
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• v.31 no.3
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• pp.39-52
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• 2024

The development of autonomous driving technology is expected to bring about a major change in the mobility rights of the transportation vulnerable. It is very important to identify user requirements in developing autonomous vehicles and service technologies for the transportation vulnerable. User requirements were derived for the wheelchair disabled and the walking disabled. Through focus interviews, a total of 58 requirements were derived for wheelchair-using disabled people and 53 requirements for walking disabled people. A Kano survey was conducted on 33 wheelchair disabled and 34 walking disabled. After that, the quality types of functional requirements in terms of autonomous vehicles and service environment development were analyzed using the Kano model. Priority analysis was conducted on the functions required by the wheelchair disabled and the walking disabled. The results of this study can be used as basic data to determine the priorities of user function requirements in the early stages of autonomous vehicle and service technology development.

• Journal of Auto-vehicle Safety Association
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• v.16 no.1
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• pp.35-41
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• 2024

In order to verify autonomous driving scenarios and safety, a lot of driving and accident data is needed, so various organizations are conducting classification and analysis of traffic accident types. In this study, it was determined that accident recording devices such as EDR (Event Data Recorder) and DSSAD (Data Storage System for Automated Driving) would become an objective standard for analyzing the causes of autonomous vehicle accidents, and traffic accidents that occurred from 2015 to 2020 were analyzed. Using the database system of IGLAD (Initiative for the Global Harmonization of Accident Data), approximately 360 accident data of EDR-equipped vehicles were classified and their characteristics were analyzed by comparing them with accident types of ADAS (Advanced Driver Assistance System)-equipped vehicles. It will be used to develop autonomous vehicle accident investigation guidelines in the future.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.138-144
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• 2020

Currently, an autonomous vehicle studies are working to develop a four-level autonomous vehicle that can cope with emergencies. In order to flexibly respond to an emergency, the autonomous vehicle must move in a direction to minimize the damage, which must be conducted by judging all the states of the road, such as the surrounding pedestrians, road conditions, and surrounding vehicle conditions. Therefore, in this paper, we suggest a passenger detection and sharing system to detect the passenger situation inside the autonomous vehicle and share it with V2V to the surrounding vehicles to assist in the operation of the autonomous vehicle. Passenger detection and sharing system improve the weighting method that recognizes passengers in the current vehicle to identify the passenger's position accurately inside the vehicle, and shares the passenger's position of each vehicle with other vehicles around it in case of emergency. So, it can help determine the driving of a vehicle. As a result of the experiment, the body pressure sensor applied to the passenger recognition sub-module showed about 8% higher accuracy than the conventional resonant sensor and about 17% higher than the piezoelectric sensor.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.297-305
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• 2017

As the development of autonomous vehicles becomes realistic, many automobile manufacturers and components producers aim to develop 'completely autonomous driving'. ADAS (Advanced Driver Assistance Systems) which has been applied in automobile recently, supports the driver in controlling lane maintenance, speed and direction in a single lane based on limited road environment. Although technologies of obstacles avoidance on the obstacle environment have been developed, they concentrates on simple obstacle avoidances, not considering the control of the actual vehicle in the real situation which makes drivers feel unsafe from the sudden change of the wheel and the speed of the vehicle. In order to develop the 'completely autonomous driving' automobile which perceives the surrounding environment by itself and operates, ability of the vehicle should be enhanced in a way human driver does. In this sense, this paper intends to establish a strategy with which autonomous vehicles behave human-friendly based on vehicle dynamics through the reinforcement learning that is based on Q-learning, a type of machine learning. The obstacle avoidance reinforcement learning proceeded in 5 simulations. The reward rule has been set in the experiment so that the car can learn by itself with recurring events, allowing the experiment to have the similar environment to the one when humans drive. Driving Simulator has been used to verify results of the reinforcement learning. The ultimate goal of this study is to enable autonomous vehicles avoid obstacles in a human-friendly way when obstacles appear in their sight, using controlling methods that have previously been learned in various conditions through the reinforcement learning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.683-686
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• 2018

Autonomous vehicles are the 4th industrial revolution that utilizes artificial intelligence(AI) and superconducting technology, and is a world-wide investment and research project. However, a Uber vehicle under test in Arizona, USA, was accidentally killed by pedestrians crossing the road in the dark night, and accidents occurred when the Tesla vehicle was exposedto the backlightof the sun. These problems were caused by misunderstandings and choice about sensors mounted on autonomous vehicles due to bad weather such as snow, rain, and sunlight. In this paper, we analyze the composition of the autonomous vehicle and the cause of the accident, and consider the criteria that should be judged in case of emergency in which human accidents may occur. This paper analyzes the composition of autonomous vehicles and causes of accidents, and considers the criteria that should be choice in an emergency where an accident may occur.

• Journal of Drive and Control
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• v.12 no.3
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• pp.36-43
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• 2015

An autonomous vehicle control algorithm based on Ackerman Geometry is known to be reliable in low tire slip situation. However, vehicles at high speed make lateral errors due to high tire slip. In this paper, considering the tire slip of vehicles, the steering angle is determined based on the Ackerman Geometry and is supplemented tire slip angle by the Stanley steering algorithm. In addition, to prevent the tire slip, the algorithm, which restricts steering if a certain level of slip occurs, is used to reduce the lateral error. While many studies have been extended to include vehicle slip, studies also need to be carried out on the tire slip depending on hardware performance. The control algorithm of autonomous vehicles is compensated considering the sensor noise and the performance of steering actuator. Through the various simulations, it was found that the performance of steering actuator was the key factor affecting the performance of autonomous driving. Also, it was verified that the usefulness of steering algorithm considering the tire slip and performance of steering actuator.

• Journal of Advanced Navigation Technology
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• v.23 no.4
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• pp.296-301
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• 2019

Artificial intelligence is closely linked to our real lives, leading innovation in various fields. Especially, as a means of transportation possessing artificial intelligence, autonomous unmanned vehicles are actively researched and are expected to be put into practical use soon. Autonomous cars and autonomous unmanned aerial vehicles are required to equip accurate navigation system so that they can find out their present position and move to their destination. At present, the navigation of transportation that we operate is mostly dependent on GPS. However, GPS is vulnerable to external intereference. In fact, since 2010, North Korea has jammed GPS several times, causing serious disruptions to mobile communications and aircraft operations. Therefore, in order to ensure safety in the operation of the autonomous unmanned vehicles and to prevent serious accidents caused by the intereference, rapid situation judgment and countermeasure are required. In this paper, based on big data and machine learning technology, we propose a countermeasure system for GPS interference that supports decision making by applying John Boyd's OODA loop cycle (detection - direction setting - determination - action).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.7-14
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• 2021

By changing the bulky monolithic services architecture to a microservices-based architecture, industries are managing the rising complexity of Autonomous Vehicles. However, the underlying communication mechanisms for the utilization and distribution of these microservices are incapable of fulfilling the requirements of the futuristic AV, because of the stringent latency requirements along with intermittent and short-lived connectivity issues. This paper proposes to tackle these challenges by employing the revolutionary information-centric networking (ICN) paradigm as an underlying communication architecture. This paper argues that a microservice approach to building autonomous vehicle systems should utilize ICN to achieve effective service utilization, efficient distribution, and uniform service discovery. This research claims that the vision of an information-centric microservices will help to focus on research that can fill in current communication gaps preventing more effective, and lightweight autonomous vehicle services and communication protocols.