Editing Autonomous Vehicles (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Object detection from LIDAR point clouds with PointPillars:'''

<syntaxhighlight lang="python">
# Conceptual pipeline — real implementations use mmdetection3d or OpenPCDet
import numpy as np
import torch

def preprocess_lidar(points, voxel_size=(0.16, 0.16, 4.0),
                      point_cloud_range=[-51.2, -51.2, -5, 51.2, 51.2, 3]):
    """
    Convert raw LIDAR point cloud to voxelized representation.
    points: (N, 4) array of [x, y, z, intensity]
    Returns: pillars tensor for PointPillars input
    """
    x_min, y_min, z_min, x_max, y_max, z_max = point_cloud_range
    vx, vy, vz = voxel_size

    # Filter points to range
    mask = ((points[:, 0] >= x_min) & (points[:, 0] < x_max) &
            (points[:, 1] >= y_min) & (points[:, 1] < y_max) &
            (points[:, 2] >= z_min) & (points[:, 2] < z_max))
    points = points[mask]

    # Assign points to pillars (vertical columns in XY grid)
    pillar_x = np.floor((points[:, 0] - x_min) / vx).astype(int)
    pillar_y = np.floor((points[:, 1] - y_min) / vy).astype(int)
    pillar_idx = pillar_x * int((y_max - y_min) / vy) + pillar_y

    return points, pillar_idx  # Feed to PointPillar encoder → BEV feature map


# Planning: Model Predictive Control (simplified)
def mpc_step(current_state, reference_trajectory, horizon=20, dt=0.1):
    """
    current_state: [x, y, heading, velocity]
    reference_trajectory: array of (x, y, heading, velocity) waypoints
    Returns optimal steering and acceleration commands
    """
    # In practice: scipy.optimize.minimize or CasADi for the optimization
    # Objective: minimize deviation from reference + control effort
    # Constraints: vehicle dynamics, comfort (max jerk), safety (no collision)
    pass  # Placeholder for the optimization
</syntaxhighlight>

; AV system architecture approaches
: '''Modular pipeline''' → Separate perception, prediction, planning, control modules. Interpretable, easier to debug. Waymo, Cruise.
: '''End-to-end learning''' → Single neural network maps sensors → control commands. Less interpretable but captures cross-module synergies. Tesla FSD, NVIDIA DRIVE.
: '''Imitation learning''' → Train on human driving demonstrations (behavioral cloning). Simple but doesn't generalize to novel situations.
: '''RL + simulation''' → Train planning policy in simulation via RL. Hard to transfer to real world (sim-to-real gap).
</div>

<div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">