Dynamic Autonomous Vehicle Routing & Sensor Telemetry AI Pipeline

import ray
import time
from typing import List, Dict
from pydantic import BaseModel

ray.init(ignore_reinit_error=True)

class RouteWaypoint(BaseModel):
    lat: float
    lng: float
    stop_id: str
    delivery_window_end: str

class VehicleState(BaseModel):
    vehicle_id: str
    current_lat: float
    current_lng: float
    speed_kmh: float
    waypoints: List[RouteWaypoint]

@ray.remote
class FleetSolverActor:
    def __init__(self, cluster_id: str):
        self.cluster_id = cluster_id

    def solve_dynamic_route(self, vehicle: VehicleState) -> Dict:
        start_time = time.time()
        
        # Simulating OR-Tools dynamic TSP solver math
        optimized_stops = sorted(
            vehicle.waypoints,
            key=lambda w: (w.lat - vehicle.current_lat)**2 + (w.lng - vehicle.current_lng)**2
        )
        
        solver_ms = (time.time() - start_time) * 1000.0
        return {
            "vehicle_id": vehicle.vehicle_id,
            "optimized_waypoints": [w.stop_id for w in optimized_stops],
            "solver_latency_ms": round(solver_ms, 2)
        }

# Batch executing parallel routing across vehicle actors in Ray
def batch_reroute_vehicles(vehicles: List[VehicleState]) -> List[Dict]:
    actors = [FleetSolverActor.remote(f"worker-{i%8}") for i in range(8)]
    futures = [
        actors[i % 8].solve_dynamic_route.remote(veh)
        for i, veh in enumerate(vehicles)
    ]
    return ray.get(futures)