GPT-5 - RoboTaxi Route Optimization
Develop a system using GPT-5 to optimize robotaxi routes in Singapore, leveraging real-time traffic data and integrating with map APIs. The system should utilize RAG capabilities to access and process information from various sources, including traffic updates, road closures, and points of interest. The GPT-5 model will be used to reason about the optimal route based on the retrieved information, considering factors like travel time, passenger preferences, and potential congestion. Few-shot learning techniques will be used to train the model on a limited dataset of route optimization examples. The final product should be a functioning prototype that can suggest optimal routes given specific parameters.
What you are building
The core problem, expected build, and operating context for this challenge.
Optimize robotaxi routes in Singapore using GPT-5's reasoning capabilities and RAG for real-time data integration.
How work is evaluated
Evaluation will be based on the accuracy and efficiency of route optimization, as well as the user-friendliness of the interface.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Utilize few-shot learning techniques to improve accuracy
Integrate real-time traffic data from various sources
Implement a Haystack pipeline for efficient knowledge retrieval
Develop a GPT-5-powered route optimization algorithm
Create a user-friendly interface for route visualization
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
Evaluation will be based on the accuracy and efficiency of route optimization, as well as the user-friendliness of the interface.
Challenge input
Start location, end location, time of day, passenger preferences (optional)
Agent execution
No agent tool is listed for this challenge.
Evaluated output
Optimal route with estimated travel time, visualized on a map
- No evaluation checks are configured.
- Route Accuracy target: 90
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- No evaluation checks are configured.
Metrics
- Route Accuracy · Target 90
- Efficiency · Target 40
Workflow tasks
- Task 1
Input: Start location, end location, time of day, passenger preferences (optional)
Output: Optimal route with estimated travel time, visualized on a map
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.