mlsanityData to 14 Oct 2026

Personalized and Context-aware Route Planning for Edge-assisted Vehicles

arXiv 2407.17980 ↗First posted 25 Jul 2024cs.AI cs.ROcited by 0

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Abstract (arXiv)

Conventional route planning services typically offer the same routes to all drivers, focusing primarily on a few standardized factors such as travel distance or time, overlooking individual driver preferences. With the inception of autonomous vehicles expected in the coming years, where vehicles will rely on routes decided by such planners, there arises a need to incorporate the specific preferences of each driver, ensuring personalized navigation experiences. In this work, we propose a novel approach based on graph neural networks (GNNs) and deep reinforcement learning (DRL), aimed at customizing routes to suit individual preferences. By analyzing the historical trajectories of individual drivers, we classify their driving behavior and associate it with relevant road attributes as indicators of driver preferences. The GNN is capable of representing the road network as graph-structured data effectively, while DRL is capable of making decisions utilizing reward mechanisms to optimize route selection with factors such as travel costs, congestion level, and driver satisfaction. We evaluate our proposed GNN-based DRL framework using a real-world road network and demonstrate its ability to accommodate driver preferences, offering a range of route options tailored to individual drivers. The results indicate that our framework can select routes that accommodate driver's preferences with up to a 17% improvement compared to a generic route planner, and reduce the travel time by 33% (afternoon) and 46% (evening) relatively to the shortest distance-based approach.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 38 references that are in the map. First posted on arXiv 25 Jul 2024.

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8 of 38 references.

References · 8

  1. 2018How Powerful are Graph Neural Networks?cited by 4,393
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  4. 2019Strategies for Pre-training Graph Neural Networkscited by 971
  5. 2019Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use casecited by 53
  6. 2021Solve routing problems with a residual edge-graph attention neural networkcited by 13
  7. 2014The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the Citycited by 9
  8. 2021Reducing Waiting Times at Charging Stations with Adaptive Electric Vehicle Route Planningcited by 3

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  3. 2023Parameterized Decision-making with Multi-modal Perception for Autonomous Driving
  4. 2023Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning
  5. 2024Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework
  6. 2025Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach
  7. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  8. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator