def sol(input_obj: Input) -> Output:
rides = sorted(
input_obj.rides,
key=lambda x:
(x.earliest_start, x.latest_finish, -distance(x.from_cell, x.to_cell)))
num_vehicles = input_obj.vehicles
id2vehicles = {k: Vehicle(k) for k in range(num_vehicles)}
for ride in rides:
# select vehicles for the current round
current_vehicles = list(id2vehicles.values())
compatible_vehicles = list(
filter(
lambda x: x.time + distance(x.position, ride.from_cell) <
input_obj.steps, current_vehicles))
if not compatible_vehicles:
continue
best_vehicle = min(compatible_vehicles,
key=lambda x:
(distance(x.position, ride.from_cell), x.time))
id2vehicles[best_vehicle.id].rides.append(ride.id)
id2vehicles[best_vehicle.id].move(ride.to_cell)
o = Output({v.id: v.rides for v in id2vehicles.values()})
return o
def test_park_vehicle(self):
# Given
vehicle = Vehicle("KA-01-HH-2701", "Red")
parking_slot = ParkingSlot(10)
# When
parking_slot.park(vehicle)
# Then
self.assertEqual(parking_slot.get_parked_vehicle(), vehicle)
def sol(input_obj: Input) -> Output:
rides = sorted(input_obj.rides,
key=lambda x: (x.earliest_start, x.latest_finish))
num_vehicles = input_obj.vehicles
vehicles = [Vehicle(k) for k in range(num_vehicles)]
for ride in rides:
# select vehicles for the current round
best_vehicle = min(vehicles,
key=lambda x:
(distance(x.position, ride.from_cell), x.time))
best_vehicle.rides.append(ride.id)
best_vehicle.move(ride.to_cell)
o = Output({v.id: v.rides for v in vehicles})
return o
def test_invalid_registration_number(self):
with self.assertRaises(ValueError):
Vehicle("invalid-number", "Color")
def test_valid_registration_number(self):
self.assertTrue(isinstance(Vehicle("KA-01-HH-2701", "Red"), Vehicle))
lengthTractor=4.0,
lengthTrailer=10.0,
tailDistance=0.5)
truck.setNoise(errorPos=2.0,
errorPhi=2.0 * pi / 180,
errorOrientation=5.0 * pi / 180,
errorVelocity=5.0)
truck.createPlot(fig, ax_simulation)
truck.tractor.v = 0
truck.tractor.phi = 0
# Refresh rate of the simulation
truck.plotRefreshRate = .05
# Create the top layer of the autonomous vehicle
sdv = Vehicle(length=4, fig=fig, ax=ax_vehicle)
# Initializes the controller
sdv.pilot.initPhiPIDController(K_p=32.0, K_i=0.4, K_d=1.0)
# Set the track to follow. We are tracking a gps reference here
sdv.planner.setTrack(x, y, angles, speedLimit, False)
def laneTracker():
global truck
return truck.tractor.x, truck.tractor.y
# connect the autonomous vehcile system to the 'physical' system
sdv.connectToSimulation(truck.tractor, laneTracker)