implementation

Gemini 2.5 Pro Integration for Obstacle Assessment

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Linked challenge: Autonomous Robotaxi Decision Agent

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Linked challenge

Autonomous Robotaxi Decision Agent

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Implement a 'assess_risk' node that uses Gemini 2.5 Pro in Deep Think mode. This node should receive simulated sensor data (distance, type of obstacle, speed) and output a recommended action ('brake', 'swerve', 'proceed_cautiously') along with a rationale. Focus on how to prompt Gemini for robust, safety-conscious reasoning and integrate its output back into the LangGraph state for subsequent actions.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

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This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Autonomous Robotaxi Decision Agent

Design and implement an advanced agentic system capable of making real-time, safety-critical decisions for autonomous robotaxis. This challenge focuses on building a robust, stateful decision-making agent that can handle complex traffic scenarios, unexpected events, and regulatory compliance by integrating real-time sensor data and regulatory knowledge bases. The system will leverage extended thinking capabilities and graph-based workflows to ensure safe and efficient operation within a simulated environment. The core of this challenge involves orchestrating a multi-agent system where a central 'Driver Agent' (powered by Gemini 2.5 Pro in Deep Think mode) uses LangGraph to manage dynamic state and decision paths. This agent will communicate with 'Sensor Agents' and 'Regulatory Agents' via a simplified A2A protocol. Tool integration will simulate vehicle controls (acceleration, braking, steering) and external data feeds (traffic updates, weather forecasts, geo-fencing policies).

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