Summary of Personalized and Context-aware Route Planning For Edge-assisted Vehicles, by Dinesh Cyril Selvaraj et al.
Personalized and Context-aware Route Planning for Edge-assisted Vehicles
by Dinesh Cyril Selvaraj, Falko Dressler, Carla Fabiana Chiasserini
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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 presents a novel approach for customizing routes in autonomous vehicles based on individual driver preferences. The proposed framework uses graph neural networks (GNNs) and deep reinforcement learning (DRL) to optimize route selection considering factors like travel costs, congestion levels, and driver satisfaction. By analyzing historical trajectories of drivers, the system classifies their driving behavior and associates it with relevant road attributes as indicators of preferences. The GNN effectively represents the road network as graph-structured data, while DRL makes decisions using reward mechanisms to optimize route selection. The framework is evaluated on a real-world road network, demonstrating its ability to accommodate driver preferences with up to 17% improvement compared to generic route planners and reducing travel time by 33% (afternoon) and 46% (evening) relative to shortest distance-based approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if your car could learn your driving habits and choose the best routes for you based on how you like to drive. This paper proposes a new way to do just that using special computer models called graph neural networks and deep reinforcement learning. By analyzing how you’ve driven in the past, these models can figure out what you prefer – whether it’s avoiding traffic or taking a shortcut. The goal is to create personalized navigation experiences for autonomous vehicles as they become more common. |
Keywords
» Artificial intelligence » Gnn » Reinforcement learning
