Effects of Wireless Communication on Integrated Risk Management Based Automated Vehicle

Shin, Donghoon; Kim, Beom-Jun; Seo, Jongsang; Yi, Kyongsu

doi:10.1109/itsc.2015.287

Cited by 7 publications

(10 citation statements)

References 15 publications

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“…With V2V communication, whenever the remote vehicle brakes, it directly transmits the deceleration information to the host vehicle. Shin et al (2) . investigated that the V2V/Radar fusion case, upon receiving the message, brakes strongly enough for safety while ensuring driver's comfort.…”

Section: Computer Simulation Studiesmentioning

confidence: 99%

“…The cut-in vehicle directly transmits yaw rate information to the host vehicle at the instant it starts to cut in from the center of the adjacent lane. According to Shin et al (2) . this means that the host vehicle will be able to have longer response time to slow down or respond with the aid of V2V communication.…”

Section: Computer Simulation Studiesmentioning

confidence: 99%

“…However, in an application of automotive radars, one of the most significant problems is an uncertain measurement model problem which is wandering on the target's physical boundary. To cope with this problem, more accurate target vehicle's information such as yaw rate, absolute velocity and acceleration needs to be transmitted using the V2V communications as reported by Shin et al (2), (9) . V2V communication is considered since it allows the enhancement of already existing ADAS or Automated Drive system.…”

Section: Introductionmentioning

confidence: 99%

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Human Factor Considered Risk Assessment of Automated Vehicle Using Vehicle to Vehicle Wireless Communication

Shin

2018

IJAE

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This paper describes a human factor considered risk assessment of automated vehicle using vehicle to vehicle wireless communication (V2V communication). A human-reaction time is incorporated into the probabilistic threat assessment algorithm for the human-centered risk assessment. The V2V communication have been fused with a radar sensor to achieve more enhanced tracking performance of automated vehicle. Information fusion of two tracks, V2V communication and radar sensor, is performed using Global Nearest Neighborhood (GNN) approach. A prediction of vehicle's motion follows the basic idea of the particle filtering. Based on the predicted behavior of vehicles, a collision risk is computed numerically and 321 driver data based human reaction time are incorporated to determine an active safety control intervention moment. The humancentered risk assessment algorithm has been applied to a collision avoidance scenario to monitor threat vehicles ahead and to find the best intervention point. Effects of the vehicular communication on a target vehicle state estimation and a vehicle safety control performance are investigated. The performance of the proposed algorithm has been investigated via computer simulation studies. It has been shown from both simulations and vehicle tests that the proposed human-centered risk assessment algorithm with the V2V communication can be beneficial to active safety systems in decision of controller intervention moment and in control of automated drive for the guaranteed safety.

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Section: Computer Simulation Studiesmentioning

confidence: 99%

Section: Computer Simulation Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Human Factor Considered Risk Assessment of Automated Vehicle Using Vehicle to Vehicle Wireless Communication

Shin

2018

IJAE

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“…Existing methods of assessment include AV focusing on risk-related figures and attributions. These methods consist of process-driven [4,[17][18][19], probability [26][27][28][29], model-based techniques [30][31][32], artificial intelligence methodologies [33][34][35] and lastly cooperative mode based approaches [36][37][38][39][40][41][42][43][44].…”

Section: Related Workmentioning

confidence: 99%

Infrastructure-Based Pedestrian Risk Tagging Methodology to Support AV Risk Assessment

et al. 2022

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“…To better estimate the trajectory of vehicle movement, the performance of the state of estimation algorithms is one of the most significant factors. Thus, various approaches that have been theorized in literature have been tested, such as dynamic Bayesian networks (DBN), hidden Markov models (HMM), support vector machines (SVM), interacting multiple model (IMM), and vehicle-to-vehicle (V2V) communication [8][9][10][11][12]. Traditional learning methods like DBN and HMM are frequently used since they are simple, fast, and do not require lots of data to become trained [13].…”

Section: Introductionmentioning

confidence: 99%

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Shin

Park

et al. 2020

Applied Sciences

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Research shows that urban intersections are a hotspot for traffic accidents which cause major human injuries. Predicting turning, passing, and stop maneuvers against surrounding vehicles is considered to be fundamental for advanced driver assistance systems (ADAS), or automated driving systems in urban intersections. In order to estimate the target intent in such situations, an interacting multiple model (IMM)-based intersection-target-intent estimation algorithm is proposed. A driver model is developed to represent the driver’s maneuvering on the intersection using an IMM-based target intent classification algorithm. The performance of the intersection-target-intent estimation algorithm is examined through simulation studies. It is demonstrated that the intention of a target vehicle is successfully predicted based on observations at an individual intersection by proposed algorithms.

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Effects of Wireless Communication on Integrated Risk Management Based Automated Vehicle

Cited by 7 publications

References 15 publications

Human Factor Considered Risk Assessment of Automated Vehicle Using Vehicle to Vehicle Wireless Communication

Human Factor Considered Risk Assessment of Automated Vehicle Using Vehicle to Vehicle Wireless Communication

Infrastructure-Based Pedestrian Risk Tagging Methodology to Support AV Risk Assessment

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

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