AI System for Transport and Autonomous Systems Development

Development of an AI system for transport and unmanned systems

Autonomous transport is one of the most capital-intensive and technically complex applications of AI. The technology stack encompasses computer vision, trajectory planning, real-time decision-making, and safety systems. Industrial drones (mining trucks, port AGVs, warehouse drones) are already in mass production.

Autonomous Driving Stack

Perception

Sensor array: LiDAR, cameras, radar, ultrasonic. Each sensor has its own strengths and weaknesses:

Fusion Architecture:

import numpy as np
import torch
from torch import nn

class SensorFusionNetwork(nn.Module):
    """
    Ранняя/поздняя fusion: LiDAR point cloud + camera image → 3D detection
    Реализация: PointPillars (быстрее PointNet для LiDAR) + ResNet для камеры
    """
    def __init__(self, n_classes=10):
        super().__init__()

        # LiDAR branch: PointPillars → BEV feature map
        self.pillar_vfe = PillarVFE(in_channels=9, out_channels=64)
        self.lidar_backbone = nn.Sequential(
            nn.Conv2d(64, 128, 3, stride=2, padding=1),
            nn.BatchNorm2d(128), nn.ReLU(),
            nn.Conv2d(128, 256, 3, stride=2, padding=1),
            nn.BatchNorm2d(256), nn.ReLU(),
        )

        # Camera branch: ResNet50 backbone → projection to BEV
        self.camera_backbone = torchvision.models.resnet50(pretrained=True)
        self.cam_to_bev = CamToBEVProjection(intrinsics=None)

        # Fusion
        self.fusion_conv = nn.Conv2d(512, 256, 1)

        # Detection head
        self.det_head = AnchorFreeDetectionHead(n_classes=n_classes)

    def forward(self, lidar_pillars, camera_imgs, lidar_indices):
        # LiDAR
        lidar_features = self.pillar_vfe(lidar_pillars)
        bev_lidar = self.scatter_to_bev(lidar_features, lidar_indices)
        lidar_out = self.lidar_backbone(bev_lidar)

        # Camera → BEV
        cam_features = self.camera_backbone.layer3(camera_imgs)
        bev_cam = self.cam_to_bev(cam_features)

        # Concat & fuse
        fused = torch.cat([lidar_out, bev_cam], dim=1)
        fused = self.fusion_conv(fused)

        return self.det_head(fused)

Semantic Segmentation:

BEV (Bird's Eye View) segmentation: each voxel/pixel is a class (road, pedestrian, car, marking). For urban environments: SegFormer-B5; for highways: lighter architectures with 99+ fps.

Prediction (prediction of behavior)

Detecting an object is only half the task. More important is predicting where it will move in the next 3–5 seconds.

Social Force Model + LSTM:

Pedestrians interact socially: they avoid collisions and follow groups. Social LSTM captures these interactions by pooling the hidden states of neighboring agents.

Transformer-based trajectory prediction:

Wayformer, MotionTransformer: attention across all agents and map elements → multi-modal probabilistic trajectory prediction (6 hypotheses × their probabilities).

Planning & Control

Path Planning:

• Global: A* or Dijkstra on HD map (Here HD Live Map, TomTom AutoStream) - Local: Lattice Planner or MPC with obstacle avoidance in the horizon of 5–10 sec - Frenet Frame: planning in the coordinate system along/across the road

MPC Controller:

class VehicleMPC:
    """
    Model Predictive Control для управления транспортным средством.
    State: [x, y, yaw, v], Control: [steering, acceleration]
    """
    def __init__(self, dt=0.1, horizon=20):
        self.dt = dt
        self.N = horizon

    def bicycle_model(self, state, control, L=2.7):
        """Кинематическая велосипедная модель ТС"""
        x, y, yaw, v = state
        delta, a = control  # угол поворота руля, ускорение

        x_new = x + v * np.cos(yaw) * self.dt
        y_new = y + v * np.sin(yaw) * self.dt
        yaw_new = yaw + v / L * np.tan(delta) * self.dt
        v_new = v + a * self.dt

        return np.array([x_new, y_new, yaw_new, np.clip(v_new, 0, 30)])

Industrial applications

Mining dump trucks (Autonomous Haul System): - Komatsu FrontRunner, Caterpillar MineStar: in serial operation since 2013 - Primary sensor: GPS RTK + LiDAR + radar - Productivity: +15% vs. manned (no fatigue, optimal speeds)

Port AGVs (Automated Guided Vehicles): - Rotterdam, Hamburg terminals: 100% AGVs for container handling - Navigation: QR codes on the floor + LiDAR - Throughput: +20–25% stacking density in the same area

Warehouses (AMR — Autonomous Mobile Robots): - Geek+, 6 River Systems, Fetch Robotics - ROS 2 (Robot Operating System) as middleware - Dynamic route planning: SLAM (Simultaneous Localization and Mapping)

Safety & Certification

Functional Safety (ISO 26262 / SOTIF ISO 21448): - ASIL D: the highest level for critical control systems - Redundancy: ECU duplication, emergency brake - Formal verification: critical algorithms are verified using formal methods

Development time: 12–24 months for a full stack of autonomous vehicles; industrial AGVs/robots – 6–12 months.