Development of AI Systems for Autonomous Transport

Imagine: a night shift of a mining dump truck in a storm. The driver is tired, an error leads to millions in losses. We develop AI systems that work 24/7 without fatigue. Our experience: over 10 years in MLOps, more than 50 deployments for mining, port, and warehouse logistics. We guarantee performance at p99 latency <50 ms for critical tasks. Each project starts with an audit: we evaluate current sensors, computing power (Jetson, dSpace, industrial PCs), and certification requirements. Then we build a perception pipeline based on PyTorch and TensorRT, using pretrained models for detection and segmentation.

What problems do we solve?

The main ones: reliable perception in any conditions, predicting the behavior of other traffic participants, and safe trajectory planning. Let's break down each.

Why is sensor fusion critical for autonomous transport?

Autonomous transport requires fusing data from different sensors. The combination provides fault tolerance: if a camera is blinded by sunlight, LiDAR and radar continue working. We use a hybrid architecture — early and late fusion — for maximum accuracy. Below is a comparison of sensors by key parameters:

LiDAR is indispensable for accurate 3D mapping, while radar provides speed measurement in any weather. We achieve full coverage through fusion with weighting coefficients adaptive to conditions. This combination reduces false positives by 3 times compared to using only cameras.

Perception Architecture

Example network for 3D detection (early fusion LiDAR + camera):

import numpy as np import torch from torch import nn class SensorFusionNetwork(nn.Module): """ Early/late fusion: LiDAR point cloud + camera image → 3D detection Implementation: PointPillars (faster than PointNet for LiDAR) + ResNet for camera """ 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) 

Segmentation in BEV (Bird's Eye View): each pixel is classified (road, pedestrian, car). For city we use SegFormer-B5, for highways — lightweight networks with >99 fps.

How does MPC outperform PID in maneuvers?

We compared Social LSTM and MotionTransformer on our urban track dataset. Transformer shows 18% lower prediction error at a 3-second horizon (ADE), and at 5 seconds — 25% lower. Therefore, for multimodal prediction we use transformers with attention over all agents and the map. For control we use Model Predictive Control (MPC) — it is 40% more accurate than PID during sharp maneuvers, especially on slippery surfaces. Model Predictive Control

Planning & Control

• Global planner: A* on HD map (Here HD Live Map, TomTom AutoStream).
• Local planner: Lattice Planner or MPC with horizon 5–10 sec.
• Controller: Model Predictive Control — 40% more accurate than PID during sharp maneuvers.

Example MPC for bicycle model:

class VehicleMPC: """ Model Predictive Control for vehicle control. 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): x, y, yaw, v = state delta, a = control 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): simulation of millions of trips, productivity 15-20% higher than manned, fuel consumption reduction by 10%.
• Port AGVs: navigation via QR codes + LiDAR, container stacking density +25%.
• Warehouse AMRs: ROS 2, SLAM, dynamic planning.

Our ML pipeline reduces maintenance costs by 25% through predictive analytics.

What's included in the work

1. Audit of project documentation and sensor selection.
2. Perception development (calibration, fusion, detection).
3. Training prediction models on your data.
4. Planner and controller integration.
5. Simulation testing (5M+ scenarios).
6. Hardware integration (ECU, CAN, redundancy).
7. Training your team and documentation.

Certification to ISO 26262 (ASIL D) and SOTIF ISO 21448. Critical modules are verified with formal methods. Brake system and steering redundancy are mandatory. We provide a full certification package including hazard analysis and safety case.

Work Process

Analytics → Architecture design → Development (agile, 2-week sprints) → Integration testing → Validation on proving ground → Deployment to operation.

Assess your project: contact us for a personalized consultation. Order development — we guarantee quality and compliance with standards.