HAULR: An Open-Source AI Copilot for Smarter Truck Trip Planning and ELD Logs

Truck drivers deal with routes, hours-of-service regulations, and electronic logs every single day. Planning a compliant trip often means juggling multiple tools and manually calculating driving and rest times. An open-source project called HAULR is trying to change that workflow with AI.

What Is HAULR?

HAULR is an AI-powered trip planning assistant built specifically for truck drivers. Its goal is straightforward: cut down the time drivers spend on complex logistics so they can focus on driving itself.

According to the project author's description on GitHub, HAULR helps drivers do three things—plan trips, understand driving and rest schedules, and generate structured ELD (Electronic Logging Device) logs. These features revolve around one core pain point: planning trips while complying with Hours of Service (HOS) regulations is both time-consuming and error-prone.

The project isn't positioned as just another general-purpose chatbot, but as a vertical tool for logistics. The author states clearly in the introduction: "The core idea is to apply AI to real-world problems, rather than building yet another chatbot that only answers general questions."

Hours of Service (HOS) regulations are a set of mandatory rules established by the Federal Motor Carrier Safety Administration (FMCSA) in the United States. Their core purpose is to limit consecutive driving time for commercial truck drivers to reduce the risk of fatigue-related driving. Key provisions include: a mandatory rest of at least 10 hours after 11 consecutive hours of driving; driving time cannot exceed the 14-hour work window; a 30-minute break is required every 8 hours, and so on. Violating HOS can result in hefty fines, points on a record, or even being put out of service, which is why trip planning must stay within the regulatory framework. This also explains why AI-generated trip plans can't be adopted directly—even a slight deviation in time calculations could lead to a violation.

ELD (Electronic Logging Device) is an electronic device installed on commercial vehicles to automatically record driving time, engine status, and mileage. Since 2017, the United States has required most commercial trucks to use ELDs instead of paper logs to reduce manual tampering and recording errors. ELD data connects directly to law enforcement and fleet management systems, making the accuracy and structure of logs critical. HAULR claims to generate structured ELD logs, suggesting it may serve as an auxiliary tool to help drivers pre-fill or verify records—but it is not a replacement for certified ELD hardware.

Technical Architecture and Implementation

HAULR uses a web-based architecture, with Python and Django on the backend handling application logic and services. The AI integration is used to assist with trip planning and natural language interaction, with workflows covering route organization, driving segment allocation, and rest interval planning. ELD logs are generated in a structured format for easy downstream use and inspection.

The overall architecture was designed with open-source collaboration in mind, making it easy for others to review, extend, and improve. That said, the author leaves an honest note: the actual models, frameworks, and inference configurations used in the project should be added to the documentation to reflect the real open-weight models and tools in use.

This kind of candor isn't common in open-source projects, but it also indicates that HAULR is still at an early stage and its documentation has room to improve.

Why Open Source Matters Especially for Projects Like This

The HAULR author spends considerable time discussing the value of open innovation. For projects involving logistics and regulatory constraints, transparency and the ability to verify underlying logic are especially critical.

Open source lets developers learn from existing tools, review implementation details, experiment with different models, and build on community work. Using open-source AI means greater flexibility in choosing models, adjusting workflows, and understanding how the system operates.

More importantly, the author emphasizes a key principle: AI can assist with planning, but compliance-related calculations should not blindly trust generated results—they must be verified against applicable regulations. This view deserves serious attention from any developer applying AI in regulated industries—models can produce plausible-sounding recommendations that are actually non-compliant, and in trucking, the cost of a compliance failure is real and high.

Development Background and Toolchain

HAULR is the author's entry for the first week of the Hacktoberfest open-source AI challenge, themed "Touch Grass." The project's development workflow uses DevRelay as part of its agentic development tooling.

The author notes that the challenge was a great opportunity to explore how open-source AI can help solve real problems and encourage more intentional use of technology. The project's GitHub repository (LakSHyaMudgal1/CargoTrip) is open, and ideas and improvement suggestions are welcome.

Practical Value and Open Questions

HAULR targets a scenario that is specific enough and painful enough—HOS compliance and ELD logs are unavoidable necessities in the North American trucking industry. Making it an open-source tool could, in theory, give small fleets and independent drivers access to intelligent planning at lower cost.

But the project is still missing a few key pieces of information: which large model is actually used, what specific role AI plays in trip planning (whether it directly generates plans or assists with verification), and whether the ELD log generation logic has been validated against industry standards. These details determine whether HAULR remains at the proof-of-concept stage or can actually be put into daily use.

At the end, the author asks the community: "What features would you add to make AI-driven trip planning more useful for truck drivers?" The question itself hints that the project is still searching for product direction.

For developers watching how AI lands in vertical industries, HAULR is an early sample worth tracking. Its architectural choices, AI integration approach, and open-source collaboration model can all serve as references for similar projects.

Github: https://github.com/LakSHyaMudgal1/CargoTrip