TrafficOptimizationScenario
The evaluation will focus on the multi-agent system's ability to optimize traffic flow, reduce congestion, and resolve routing conflicts in a simulated urban environment, emphasizing inter-fleet A2A communication, adaptive reasoning, and resilience.
Evaluation overview
How the linked challenge is judged: tasks, benchmarks, and criteria count.
Task templates
Inputs and expected outputs.
TrafficOptimizationScenario
Simulates a complex traffic scenario (e.g., unexpected road closure, major event impacting multiple routes) and evaluates how effectively the A2A coordinated robotaxi fleets (simulated Waymo and Baidu) adjust routes to minimize travel time and congestion, demonstrating few-shot adaptability.
{"traffic_state": {"road_closures": ["string", ...], "event_locations": ["string", ...], "current_congestion": {"street_name": "level"}, "new_patterns_examples": [{"input": "scenario_desc", "output": "expected_agent_action"}, ...]}, "fleet_A_trips": [{"start": "loc", "end": "loc", "id": "uuid"}], "fleet_B_trips": [{"start": "loc", "end": "loc", "id": "uuid"}]}
{"fleet_A_new_routes": [{"id": "uuid", "optimized_path": ["node", ...]}], "fleet_B_new_routes": [{"id": "uuid", "optimized_path": ["node", ...]}], "a2a_communication_logs": [{"sender": "string", "receiver": "string", "message": "string", "timestamp": "datetime"}], "deepseek_reasoning_logs": "string"}