Current and Future ITS

Sai, Seii; Altintas, Onur; Kenney, John B.; Tanaka, Hideaki; Inoue, Yoshio

doi:10.1587/transinf.e96.d.176

Cited by 14 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sai et al [7] reported the development of a current and future intelligent transportation system (ITS) in which the safety initiatives included preventing traffic crashes via cooperation between vehicles based on road infrastructure. Ito et al [8] analyzed the differences between side-impact car-and-cyclist crashes and near crashes based on an analysis of driving recorders.…”

Section: Related Workmentioning

confidence: 99%

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Chang

Chen

2019

IEEE Access

View full text Add to dashboard Cite

Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96% and that the average response time for emergencyrelated announcements is approximately 7 s. INDEX TERMS Advanced driver assistance system (ADAS), artificial intelligence over Internet of Things (AIoT), automotive, deep learning, Internet of Vehicles (IoV), head-on and single-vehicle accident detection.

show abstract

Section: Related Workmentioning

confidence: 99%

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Chang

Chen

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Intelligent vehicle (IV) is an important part of intelligent transportation system (ITS), 1 – 3 which is carried with advanced vehicular sensors, controllers, and actuators and combined with modern communication and network technology. It is equipped to conduct road environment perception, 4 path planning, 5 and automatic control.…”

Section: Introductionmentioning

confidence: 99%

Expected yaw rate–based trajectory tracking control with vision delay for intelligent vehicle

Xia

Chen

et al. 2020

Science Progress

View full text Add to dashboard Cite

Accurate and real-time position of preview point is significant to trajectory tracking control of vision-guided intelligent vehicle. The unavoidable delay of road automatic identification system weakens trajectory tracking control performance, and even deteriorates the vehicle stability. Therefore, a compensator for the delay of road automatic identification system was proposed which combines the current statistical model and adaptive Kalman predictor to estimate the state of preview point position. The trajectory tracking sliding mode controller of intelligent vehicle is established through a 2–degrees of freedom vehicle dynamic model and motion model by using MATLAB/Simulink and CarSim. The trajectory tracking performance under 20–100 ms delay is analyzed. The simulation results show that the trajectory tracking performance of intelligent vehicle will be affected by the delay of road automatic identification system, reducing tracking accuracy. And when the delay is too large, it will deteriorate the vehicle stability and safety. In addition, the simulation results also verify the effectiveness of current statistical–adaptive Kalman predictor compensator at different delays.

show abstract

“…As the rapid integration of new generation information technology, sensor, intelligent control and automotive products, intelligent automobile technology has become one of the hot researches in today's automobile engineering [1, 2]. Its critical issues lie in perception, motion planning and control [3, 4].…”

Section: Introductionmentioning

confidence: 99%

Trajectory planning and optimisation method for intelligent vehicle lane changing emergently

Jiang

Shi

Cai

et al. 2018

IET Intelligent Transport Systems

View full text Add to dashboard Cite

Current and Future ITS

Cited by 14 publications

References 11 publications

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Expected yaw rate–based trajectory tracking control with vision delay for intelligent vehicle

Trajectory planning and optimisation method for intelligent vehicle lane changing emergently

Contact Info

Product

Resources

About