HAULR: AI-Powered HOS Trip Planner & ELD Log Generator 🚛
This is my submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.
What I Built
I built HAULR, an AI-powered trip planning assistant designed to help truck drivers spend less time dealing with complicated logistics and more time focusing on the road.
Truck drivers often have to manage routes, Hours of Service (HOS) regulations, rest breaks, and Electronic Logging Device (ELD) records. Planning a trip while staying compliant can be time-consuming and stressful.
HAULR aims to simplify this process by helping drivers plan trips, understand driving and rest schedules, and generate structured ELD logs.
The goal is to make logistics easier through AI while encouraging a healthier balance between screen time and real-world activities. Instead of constantly switching between tools and manually calculating schedules, drivers can use a more streamlined workflow to plan their journeys.
Code
🚀 Github: https://github.com/LakSHyaMudgal1/CargoTrip
Explore the code, understand the implementation, and feel free to contribute ideas or improvements!
How I Built It
I built HAULR around a web-based architecture with a focus on practical AI integration and useful automation.
- Backend: Python and Django for application logic and backend services.
- AI integration: An AI-powered workflow to assist with trip planning and natural-language interactions.
- Trip planning: A workflow for organizing routes, driving periods, and rest breaks.
- ELD logs: Structured generation of trip and driving-log information.
- Open-source development: An architecture designed to make the project easier to inspect, extend, and improve.
The core idea was to apply AI to a real-world problem rather than build another chatbot that only answers general questions.
Note: The exact model, framework, and inference setup should be added here to reflect the open-weight model and tools actually used in the project.
Why Does Open Innovation Matter?
Open innovation makes projects like HAULR possible because developers can learn from existing tools, inspect implementations, experiment with different models, and build on the work of the community.
Using open-source AI can provide greater flexibility in choosing models, adapting workflows, and understanding how the system operates. It also creates opportunities for contributors to improve reliability, add features, and tailor the solution to different requirements.
For a project involving logistics and regulatory constraints, transparency and the ability to validate the underlying logic are especially valuable. AI can assist with planning, but compliance-critical calculations should be checked against applicable regulations rather than blindly trusting generated responses.
This challenge was a great opportunity to explore how open-source AI can help solve a practical problem and encourage people to use technology more intentionally.
My Agent Session
I used DevRelay as part of my agentic development workflow.
Prize Categories
- Open-Source AI
- AI-powered real-world applications
Final Thoughts
Building HAULR reinforced my belief that AI is most useful when it solves a specific problem and fits naturally into people's everyday workflows.
I'd love to hear your feedback! What features would you add to make AI-powered trip planning more useful for truck drivers?
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