Corpus ID: 235485593

Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications

  title={Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications},
  author={Ruimin Ke and Zhiyong Cui and Yanlong Chen and Meixin Zhu and Hao Yang and Yinhai Wang},
Traffic near-crash events serve as critical data sources for various smart transportation applications, such as being surrogate safety measures for traffic safety research and corner case data for automated vehicle testing. However, there are several key challenges for near-crash detection. First, extracting nearcrashes from original data sources requires significant computing, communication, and storage resources. Also, existing methods lack efficiency and transferability, which bottlenecks… Expand

Figures and Tables from this paper


A Smart, Efficient, and Reliable Parking Surveillance System With Edge Artificial Intelligence on IoT Devices
This study investigates the feasibility of using edge computing for smart parking surveillance tasks, specifically, parking occupancy detection using the real-time video feed and results are promising that the final detection method achieves over 95% accuracy in real-world scenarios with high efficiency and reliability. Expand
CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning
A novel method called CyclingNet is introduced for detecting cycling near misses from video streams generated by a mounted frontal camera on a bike regardless of the camera position, the conditions of the built, the visual conditions and without any restrictions on the riding behaviour. Expand
Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment.
It is discovered that sparse but adversarial adjustments to the naturalistic driving environment can significantly reduce the required test miles without loss of evaluation unbiasedness. Expand
Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video
An advanced framework consisting of three functional modules consisting of multiple processing streams and the interconnections among them are carefully designed with the consideration of UAV video features and traffic flow characteristics to support advanced traffic sensing and management. Expand
Edge-Based Traffic Flow Data Collection Method Using Onboard Monocular Camera
This data shows clear trends in the growth in the sophistication of traffic data collection methods, and these methods have become more robust and divergent in recent years. Expand
Identifying Near-Miss Traffic Incidents in Event Recorder Data
This paper proposes a method that can automatically identify near-misses with objects such as pedestrians and bicycles by processing the ER data and demonstrates that the proposed method can accurately identify and tagNear-miss events. Expand
The 4th AI City Challenge
The 4th annual edition of the AI City Challenge has attracted 315 participating teams, who leverage city-scale real traffic data and high-quality synthetic data to compete in four challenge tracks, and results show promise that AI technology can enable smarter and safer transportation systems. Expand
Predict Vehicle Collision by TTC From Motion Using a Single Video Camera
The fine velocity computation yields reasonable TTC accuracy so that a video camera can achieve collision avoidance alone from the size changes of visual patterns. Expand
Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow
A new and complete analysis framework for traffic flow parameter estimation from UAV video addresses the well-concerned issues on UAV’s irregular ego-motion, low estimation accuracy in dense traffic situation, and high computational complexity by designing and integrating four stages. Expand
Towards Corner Case Detection for Autonomous Driving
This paper provides a formal definition of a corner case and proposes a system framework for both the online and the offline use case that can handle video signals from front cameras of a naturally moving vehicle and can output a corners case score. Expand