# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 5 Conclusions  
We have addressed the challenges of path planning for autonomous vehicles within urban environments, prioritizing both timely data transfer and efficient travel. Our novel reinforcement learning solution effectively navigates these complex challenges, demonstrating significant advantages over baseline methods. To evaluate this approach, we implemented a GraphML-based simulation environment that integrates real-world map data and vehicle mobility patterns. This environment accurately represented the dynamic and complex nature of urban traffic, providing a robust platform to test our reinforcement learning strategies. Our findings demonstrate that our reinforcement learning approach is not only viable but also advantageous for path planning in environments characterized by heterogeneous traffic and high data transfer demands. We have shown that our proposed solution is scalable and consistently outperforms standard baseline models across different trip lengths. Despite these encouraging results, several limitations should be acknowledged. First, all performance curves were generated from a single random seed; although this seed is representative, reinforcement learning outcomes can vary significantly with initialization. To address this, future work will average results over multiple seeds, reporting mean performance metrics with corresponding 95% confidence intervals to ensure full statistical robustness. Another limitation is the static nature of traffic and bandwidth conditions within individual simulation episodes and the evaluation involving only a single vehicle. To enhance realism and adaptability, future studies will incorporate online perturbations, such as sudden congestion spikes, temporary bandwidth blackouts, and forced rerouting scenarios. Additionally, expanding the simulation environment to include multiple autonomous vehicles interacting on the same road network and sharing wireless spectra will allow for an investigation into cooperative routing and resource allocation strategies. To further improve our model, replacing the current tabular critic with a graph-aware deep Q-network or an actor-critic architecture is a logical next step. This modification would facilitate end-to-end learning directly from continuous traffic and bandwidth features while preserving the scalability demonstrated in this work. Furthermore, broadening the baseline comparisons by benchmarking against advanced deterministic algorithms  
Figure 10: Map size comparison. such as time-dependent A* and multi-objective label-setting algorithms would provide richer context for evaluating our reinforcement learning approach. Additionally, transitioning from a fixed-weight scalarization approach to explicit multi-objective reinforcement learning techniques, such as Pareto-front approximation or constrained-policy optimization, would allow fleet operators to visualize and select from the complete trade-off curve between travel time and data throughput rather than relying on a single pre-tuned weight vector. Finally, an exciting extension of our framework involves incorporating electric vehicle (EV) energy management. By augmenting the state space with a battery-level variable and integrating \"visit-charging-station\" actions at nodes equipped with charging stations, future agents could learn policies that optimize travel time, data transfer, and energy replenishment simultaneously. This enhancement would highlight the generalizability of our reinforcement learning framework to broader smart mobility challenges and align closely with contemporary EV routing research trends. Given the additive nature of our reward function, incorporating battery management penalties and charging station costs would require only minor modifications, enabling seamless integration into the existing framework.