• Journal of Auto-vehicle Safety Association
• /
• v.14 no.4
• /
• pp.35-42
• /
• 2022

This paper presents a stochastic model predictive control (SMPC) for stop maneuver of autonomous vehicles considering perception uncertainty of stopped vehicle. The vehicle longitudinal motion should achieve both driving comfortability and safety. The comfortable stop maneuver can be performed by mimicking acceleration profile of human driving pattern. In order to implement human-like stop motion, we propose a reference safe inter-distance and velocity model for the longitudinal control system. The SMPC is used to track the reference model which contains the position uncertainty of preceding vehicle as a chance constraint. We conduct simulation studies of deceleration scenarios against stopped vehicle in urban environment. The test results show that proposed SMPC can execute comfortable stop maneuver and guarantee safety simultaneously.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.2
• /
• pp.115-120
• /
• 2015

Recently, in automotive technology area, intelligent safety systems have been actively accomplished for drivers, passengers, and pedestrians. Also, many researches are focused on development of autonomous vehicles. This paper propose the application of LiDAR sensors, which takes major role in perceiving environment, terrain classification, obstacle data clustering method, and local map building for autonomous driving. Finally, based on these results, planning for lane change path that vehicle tracking possible were created and the reliability of path generation were experimented.

• The Journal of the Korea Contents Association
• /
• v.19 no.12
• /
• pp.452-459
• /
• 2019

In order for an autonomous vehicle to be commercialized, it is necessary to conduct a safety test for every aspect. Considering the implementation of the autonomous vehicles technologies to the highest level, it is necessary to analyze the possible scenarios in the most complex environment as in the urban area. It should be confirmed whether autonomous vehicles can be operated with conventional traffic signal environment. It is also required to confirm the ability of autonomous vehicles in interacting with other vehicles, and coping with possible accidents on the road. In this study, the evaluation factors of autonomous vehicles in the road environment are selected by referring to the other evaluation protocols like ADAS. Study result would be reflected in establishing the autonomous vehicle evaluation method for different test environment along with various technology implementation level.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.1
• /
• pp.51-63
• /
• 2022

Convoy driving is one of the technologies of multi-vehicle cooperation driving along with platoon driving. All over the world, research on vehicle control mechanisms to maintain vehicle formation during convoy driving convoy driving has been actively conducted and in Europe's Autonet 2030 project has developed and demonstrated convoy driving services for highways. But, even the concept of convoy driving is still insufficient in Korea. Therefore, in this study, the concept of convoy driving service was established and scenarios and communication messages for service application on urban roads were developed. And its effectiveness was verified through simulation analysis. As a result of comparing and analyzing individual vehicle cooperative driving and convoy driving for the blind spot support service and dilemma zone safety support service, which are representative V2I cooperative driving services on urban roads, the number of conflicts(indicator of traffic safety) and delays and stops(indicator of traffic efficiency) are significantly improved in convoy driving compared to individual vehicle cooperative driving.

• Environmental Engineering Research
• /
• v.25 no.3
• /
• pp.366-373
• /
• 2020

The autonomous vehicles (AVs) could make a positive or negative impact on reducing mobile emissions. This study investigated the changes of mobile emissions that could be caused by large-scale adoption of AVs. The factors of road capacity increase and speed limit increase impacts were simulated using a conceptual modeling approach that combines a hypothetical speed-emission function and a traffic demand model using a virtual transportation network. The simulation results show that road capacity increase impact is significant in decreasing mobile emissions until the market share of AVs is less than 80%. If the road capacity increases by 100%, the mobile emissions will decrease by about 30%. On the other hand, driving speed limit increase impact is significant in increasing mobile emissions, and the environmentally desirable speed limit was found at around 95 km/h. If the speed limit increases to 140 km/h, the mobile emissions will increase by about 25%. This is because some vehicles begin to bypass the congested routes at high speeds as speed limit increases. Based on the simulation results, it is clear that the vehicle platooning technology implemented at reasonable speed limit is one of the AV technologies that are encouraging from the environmental point of view.

• Journal of Auto-vehicle Safety Association
• /
• v.11 no.3
• /
• pp.37-42
• /
• 2019

This paper presents an autonomous acceleration planning algorithm for pedestrian collision avoidance at urban. Various scenarios between pedestrians and a vehicle are designed to maneuver the planning algorithm. To simulate the scenarios, we analyze pedestrian's behavior and identify limitations of fusion sensors, lidar and vision camera. Acceleration is optimally determined by considering TTC (Time To Collision) and pedestrian's intention. Pedestrian's crossing intention is estimated for quick control decision to minimize full-braking situation, based on their velocity and position change. Feasibility of the proposed algorithm is verified by simulations using Carsim and Simulink, and comparisons with actual driving data.

• Journal of the Society of Disaster Information
• /
• v.17 no.1
• /
• pp.165-175
• /
• 2021

Purpose: The purpose of this study is to quantitatively present the carbon dioxide(CO₂) emission change according to the application of autonomous driving technology at the network level for waste vehicles in the metropolitan area. Method: The target year was set to 2030, and the analysis method estimated the carbon dioxide (CO₂) emissions for each road link through user equilibrium assignment when unapplied scenario. The application scenario performed traffic assignment using route data on the premise that the group was running in accordance with the application of autonomous driving technology to waste vehicles. In addition, the other means estimated the carbon dioxide emissions through user balance allocation by reflecting the results of the waste vehicle allocation. Result: As a result of the analysis, carbon dioxide(CO₂) emissions were found to be reduced by about 56.9ton/day from the national network level, and the Seoul metropolitan area was analyzed to be reduced by about 54.7ton/day. Conclusion: This study quantitatively presented environmental impacts among various social effects that autonomous driving technology will bring, and in the future, development of various analytical methodologies and related studies should be continuously conducted.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.10
• /
• pp.803-810
• /
• 2016

This paper presents a study of localization methods based on particle filter using 2D laser sensor measurements and road feature map information, for autonomous vehicles. In order to navigate in an urban environment, an autonomous vehicle should be able to estimate the location of the ego-vehicle with reasonable accuracy. In this study, road features such as curbs and road markings are detected to construct a grid-based feature map using 2D laser range finder measurements. Then, we describe a particle filter-based method for accurate positional estimation of the autonomous vehicle in real-time. Finally, the performance of the proposed method is verified through real road driving experiments, in comparison with accurate DGPS data as a reference.

• Journal of Auto-vehicle Safety Association
• /
• v.13 no.1
• /
• pp.26-30
• /
• 2021

K-city is an experimental area for developing self-driving cars. V2X communications such as WAVE, C-V2X and 5G are an essential technology for autonomous driving above level 4. In this paper, the research on the V2V communication environment was carried out through BSM receiving level analysis on the driving route in K-city. A stationary vehicle communicated with a test vehicle moving along urban area and suburban road in two different scenarios. The communication range and receiving levels obtained from this study will be used to develop and verify various safety scenarios using V2V communication within K-city in the future.

• Journal of Drive and Control
• /
• v.17 no.4
• /
• pp.72-79
• /
• 2020

To ensure the safety of Advanced Driver Assistance Systems (ADAS) or autonomous vehicles, it is important to recognize the vehicle position, and specifically, the increased accuracy of the lateral position of the vehicle is required. In recent years, the quality of GPS signals has improved a lot and the price has decreased significantly, but extreme urban environments such as tunnels still pose a critical challenge. In this study, we proposed stable and precise lane recognition and tracking methods to solve these two issues via fusion of AVM images and vehicle sensor data using an extended Kalman filter. In addition, the vehicle's lateral position recognition and the abnormal state of RTK GPS were determined using this approach. The proposed method was validated via actual vehicle experiments in urban areas reporting multipath and signal disconnections.