Summary of Vulnerable Road User Detection and Safety Enhancement: a Comprehensive Survey, by Renato M. Silva et al.
Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey
by Renato M. Silva, Gregório F. Azevedo, Matheus V. V. Berto, Jean R. Rocha, Eduardo C. Fidelis, Matheus V. Nogueira, Pedro H. Lisboa, Tiago A. Almeida
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive survey of state-of-the-art technologies and methodologies is provided to enhance the safety of vulnerable road users (VRUs) in traffic incidents. The study explores communication networks between vehicles and VRUs, emphasizing sensor integration and relevant datasets. Preprocessing techniques and data fusion methods are used to improve sensor data quality. Critical simulation environments for developing and testing VRU safety systems are assessed. Recent advances in VRU detection and classification algorithms are discussed, addressing challenges like variable environmental conditions. Predicting VRU intentions and behaviors is crucial for proactive collision avoidance strategies. The paper aims to provide a comprehensive understanding of the current landscape of VRU safety technologies, identifying areas of progress and those needing further research and development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Traffic accidents involving vulnerable road users are a big problem worldwide. To make roads safer, researchers are using new technology and machine learning techniques to analyze data from sensors like cameras and radar. This paper looks at the latest ways to improve VRU safety by studying how vehicles communicate with pedestrians, cyclists, and motorcyclists. The study also explores ways to improve sensor data quality and develop simulation environments for testing VRU safety systems. Additionally, it discusses recent advances in detecting and classifying VRUs, predicting their intentions and behaviors to prevent collisions. |
Keywords
» Artificial intelligence » Classification » Machine learning
