Summary of Edge-assisted Ml-aided Uncertainty-aware Vehicle Collision Avoidance at Urban Intersections, by Dinesh Cyril Selvaraj et al.
Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections
by Dinesh Cyril Selvaraj, Christian Vitale, Tania Panayiotou, Panayiotis Kolios, Carla Fabiana Chiasserini, Georgios Ellinas
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework for detecting and predicting collisions at urban intersections using Connected Vehicles (CVs) and Multi-access Edge Computing (MEC) platform. The Intersection Manager (IM) collects data from vehicles and infrastructure to create a holistic view of the intersection, leveraging an encoder-decoder recurrent neural network to predict vehicle trajectories with high accuracy. However, accuracy is not enough; trajectory predictions are associated with uncertainty measures for confident collision forecasting and avoidance. This framework detects collisions well in advance and reliably, triggering alarms to signal colliding vehicles to brake. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make roads safer by predicting and preventing car accidents at busy intersections using special connected vehicles and a powerful computer system. The system collects information from all the cars and the road itself to create a big picture of what’s happening. Then, it uses a super smart computer model to predict where each car will go next. But it doesn’t just stop there – it also tells us how sure it is that two cars might crash. If an accident is likely to happen, the system sends alerts to the drivers’ cars so they can slow down and avoid the crash. |
Keywords
» Artificial intelligence » Encoder decoder » Neural network
