Custom AI ADAS Module Development: FCW, LDW, BSM

Ever had the autopilot brake too late on the highway, or mistake a pedestrian for a tree? We solve these problems with custom neural networks on embedded controllers. Over the last 7 years we've delivered 15+ ADAS functions—from AEB to blind spot monitoring—that work in real conditions: rain, snow, poor lane markings. Below is the concrete stack and architecture.

Each function is a separate computer vision problem with tight latency and accuracy requirements. For AEB, the delay must not exceed 30 ms—at 100 km/h, the car covers an extra 2.8 meters every 30 ms.

We use proven models: YOLOv8n for detection, CLRNet for lanes, Depth Anything for monocular depth. All wrapped into TensorRT or ONNX Runtime for inference on NVIDIA Orin and Qualcomm Snapdragon.

The main challenge is balancing accuracy and speed, especially when running 4–5 functions concurrently. On one project we cut AEB latency from 45 ms to 22 ms by replacing the backbone with EfficientNet-lite and switching to TensorRT INT8.

Key Functions and Implementation

import cv2
import numpy as np
from ultralytics import YOLO
import torch

class ADASSystem:
    def __init__(self, config: dict):
        self.lane_detector = self._load_lane_model(config)
        self.object_detector = YOLO(config['object_model'])  # YOLOv8n для скорости
        self.depth_estimator = self._load_depth_model(config)

        self.camera_matrix = np.array(config['camera_intrinsics'])
        self.focal_length = self.camera_matrix[0, 0]
        self.baseline = config.get('stereo_baseline', None)

        # Пороги для предупреждений
        self.ttc_warning = 2.5   # секунды — предупреждение
        self.ttc_critical = 1.5  # секунды — AEB
        self.lane_offset_threshold = 0.3  # метра

    def lane_departure_warning(self, frame: np.ndarray,
                                vehicle_speed: float) -> dict:
        """
        Детекция полосы: классика — UFLD (Ultra-Fast Lane Detection)
        или CLRNet для сложных условий (пересечения, плохая разметка).
        """
        lanes = self.lane_detector(frame)
        if len(lanes) < 2:
            return {'warning': False, 'reason': 'no_lanes'}

        # Центр автомобиля относительно полосы
        frame_center = frame.shape[1] // 2
        lane_center = (lanes[0][-1][0] + lanes[1][-1][0]) // 2
        offset_px = frame_center - lane_center

        # Перевод пикселей в метры через гомографию
        offset_m = offset_px * (3.5 / abs(lanes[1][-1][0] - lanes[0][-1][0]))

        warning = abs(offset_m) > self.lane_offset_threshold
        return {
            'warning': warning,
            'offset_meters': offset_m,
            'lane_width': abs(lanes[1][-1][0] - lanes[0][-1][0])
        }

    def collision_warning(self, frame: np.ndarray,
                           ego_speed: float) -> dict:
        detections = self.object_detector(frame, conf=0.5,
                                           classes=[0, 2, 3, 5, 7])
        depth_map = self.depth_estimator(frame)

        warnings = []
        for box in detections[0].boxes:
            x1, y1, x2, y2 = map(int, box.xyxy[0])
            cx = (x1 + x2) // 2

            # Дистанция из depth map (стерео или монокулярная)
            roi_depth = depth_map[y1:y2, x1:x2]
            distance = float(np.percentile(roi_depth, 10))  # ближняя часть объекта

            # TTC при текущей скорости
            if distance > 0 and ego_speed > 0:
                ttc = distance / ego_speed  # упрощённо, без учёта скорости объекта
            else:
                ttc = float('inf')

            if ttc < self.ttc_critical:
                action = 'AEB'
            elif ttc < self.ttc_warning:
                action = 'WARNING'
            else:
                continue

            warnings.append({
                'class': self.object_detector.model.names[int(box.cls)],
                'distance_m': distance,
                'ttc_sec': ttc,
                'action': action
            })

        return {'warnings': sorted(warnings, key=lambda x: x['ttc_sec'])}

How AEB Works

When an object is detected at less than critical distance (TTC < 1.5 s), the system activates braking. We combine stereo vision and monocular depth to estimate distance. In practice, at 60 km/h, distance accuracy is ±1.5 m at 20 m range.

Why Latency Matters More Than Accuracy

At 100 km/h, the car travels 27.8 m per second. A 100 ms system delay means 2.78 m of blind travel. Therefore for ADAS:

YOLOv8n with TensorRT FP16 on NVIDIA Orin: 3–5 ms per frame. On Qualcomm SA8295P (Snapdragon Ride): 8–12 ms via QNN SDK.

Source: ISO 26262, AEB response time requirements

Model Comparison: YOLOv8n vs YOLOv8s

For AEB, YOLOv8n is better: its latency on TensorRT INT8 is 5–8 ms on NVIDIA Orin, 2.5 times faster than YOLOv8s. Meanwhile, mAP (0.5) drops from 0.52 to 0.48—negligible in practice. For BSW we use YOLOv8s (latency up to 100 ms).

Monocular Depth Estimation

If no stereo camera is available, we use MonoDepth2 or DPT (Dense Prediction Transformer). Accuracy is lower than stereo but sufficient for warnings:

from transformers import AutoImageProcessor, AutoModelForDepthEstimation

class MonocularDepth:
    def __init__(self):
        self.processor = AutoImageProcessor.from_pretrained(
            "LiheYoung/depth-anything-large-hf"
        )
        self.model = AutoModelForDepthEstimation.from_pretrained(
            "LiheYoung/depth-anything-large-hf"
        )

    @torch.no_grad()
    def estimate(self, image: np.ndarray) -> np.ndarray:
        inputs = self.processor(images=image, return_tensors="pt")
        outputs = self.model(**inputs)
        depth = outputs.predicted_depth.squeeze().numpy()
        # Масштабируем в метры через калибровочный коэффициент
        return depth

Depth Anything v2 Large gives AbsRel = 0.076 on KITTI—sufficient for distance estimation within ±10% at 10–30 m.

How We Implement the ADAS System

1. Analyze requirements and select sensors (cameras, radars, lidars).
2. Collect and annotate data for target scenarios (urban, highway, night).
3. Train models with fine-tuning and quantization (INT8/FP16).
4. Deploy on target hardware (NVIDIA Orin, Qualcomm Snapdragon, TI TDA4).
5. Validate offline on datasets and online in real conditions.
6. Document and hand over to the client.

What Is Included in the Project

• System architecture and sensor selection (cameras, radars, lidars)
• Datasets: collection, annotation, augmentation
• Model training: fine-tuning, quantization (INT8/FP16), pruning
• Deployment on target hardware (NVIDIA Orin, Qualcomm Snapdragon, TI TDA4)
• Validation: offline (on datasets) and online (real scenarios)
• Documentation: functional spec, test plan, CI/CD pipeline
• Client team training

Certification and Standards

ADAS systems for production vehicles must comply with:

• ISO 26262 (Functional Safety, ASIL-B/C for AEB)
• ISO/SAE 21434 (Cybersecurity)
• UNECE R79/R130 (regulatory requirements for LDW and AEB)

For in-plant or carport applications (not public road), requirements are softer—we use automotive grade without full ISO 26262 certification.

Timelines and Investment

Development of a single module (e.g., AEB) requires a certain investment; contact us for a tailored estimate. Order a pilot project for 1–2 functions — turnaround 6–8 weeks.

Our Experience

Over 7 years in embedded vision, 15+ ADAS projects (from prototypes to pre-series). We use automotive-grade approaches: ROS 2, DDS, SafeRTOS.

Why Choose Us

We don't just train a model—we take it to production on your specific hardware, accounting for thermal profiles and energy budget.