- OpenCV 4.x / C++17 & Python 3.10
- Inverse Perspective Mapping (IPM) & 2nd-Order Polyfit
- 77 GHz FMCW Camera-Radar Spatial Fusion
- Real-Time 30+ FPS / AEB & LDW Decision Engine
AUTOMOTIVE COMPUTER VISION & SENSOR FUSION / OPENCV & 77 GHZ RADAR PERCEPTION
Industrial Computer Vision Perception Stack: Road Geometry, Obstacle Tracking & Camera-Radar Fusion
AutoVision-ADAS is an industrial-grade perception pipeline engineered for autonomous driving and advanced driver assistance systems (SAE Level 2+/3). Built on OpenCV (C++17 and Python 3.10) with OpenCV DNN acceleration, the system processes front-facing automotive video streams at over 30 FPS. It computes road geometry and Lane Departure Warnings (LDW) via Inverse Perspective Mapping (IPM) and 2nd-order polynomial fitting, detects dynamic road agents with deep learning models, computes optical expansion Time-to-Collision (TTC), and performs extrinsic sensor fusion with front-bumper 77 GHz FMCW radar tracks to eliminate vision-only failure modes under poor lighting, fog, and sun glare.
Camera Calibration, Undistortion & IPM Bird's-Eye View
Corrects lens radial and tangential distortion via camera matrix K and distortion coefficients [k1, k2, p1, p2]. The calibrated frame is remapped into an orthogonal bird's-eye view using an Inverse Perspective Mapping (IPM) homography matrix M_ipm = cv::getPerspectiveTransform(src, dst), eliminating perspective foreshortening across a 30-meter longitudinal road envelope.
Adaptive Color Thresholding & 2nd-Order Lane Polyfit
Combines directional Sobel-X gradient filtering with S-channel (HLS) and B-channel (LAB) color spaces to isolate lane markings across challenging shadow transitions. A sliding histogram window algorithm identifies left and right boundary pixels, fitting independent 2nd-degree polynomials x = A·y² + B·y + C to calculate instantaneous road curvature radius R_curve and lateral vehicle offset from the lane centerline.
OpenCV DNN Obstacle Perception & Monocular TTC
Leverages the lightweight cv::dnn::Net module running optimized YOLOv8-Nano / MobileNetV3-SSD ONNX models with SIMD vectorization and Non-Maximum Suppression (NMS). Obstacle distance is estimated using calibrated pinhole geometry (Z_est = f_y · H_real / h_bbox), complemented by sparse Lucas-Kanade optical flow feature tracking to derive vision-based expansion-rate Time-to-Collision (TTC).
Extrinsic 77 GHz Camera-Radar Spatial Fusion & AEB
Fuses 3D kinematic tracks [X_r, Y_r, Z_r, 1]ᵀ from the 77 GHz FMCW Extended Kalman Filter into camera pixel space [u, v, 1]ᵀ using the extrinsic calibration matrix s·p = K·[R|t]·P_radar. Validated radar detections provide Doppler-accurate radial velocity and centimeter range, triggering Forward Collision Warning (FCW < 2.6s) and Autonomous Emergency Braking (AEB < 1.4s / 0.8s) with zero false-alarm rate.
Interactive AutoVision-ADAS Perception Notebook (Google Colab)
Execute the complete camera perception and camera-radar sensor fusion stack directly in your browser on Google Colab GPU/CPU cloud runtimes. Experiment with bird's-eye view perspective transforms, lane boundary polynomial tracking, deep learning bounding box predictions, and coordinate-frame projection of 77 GHz FMCW radar targets.
Lens Undistortion & IPM Bird's-Eye View Homography
Undistorts 1080p automotive video frames and executes perspective warp into a top-down orthogonal road projection, preserving real-world metric scale (30 m longitudinal × 3.7 m lane width).
Sliding Window Histogram & 2nd-Degree Polyfit Lane Tracking
Applies adaptive HLS/LAB color masks and fits quadratic curves x = A·y² + B·y + C. Calculates instantaneous road curvature (R_curve > 1,000 m) and triggers Lane Departure Warning when offset > 0.4 m.
Deep Learning Obstacle Perception & Monocular Distance / TTC
Runs YOLOv8-Nano via OpenCV DNN engine. Projects pseudo-3D bounding cuboids and estimates Time-to-Collision via bounding box optical expansion rate Δt / (S_k / S_k-1 - 1).
77 GHz Radar-to-Camera Coordinate Projection & Safety Decision FSM
Projects 3D radar point clouds and tracks onto the image plane using s·p = K·[R|t]·P_radar. Performs IoU spatial gating with camera bounding boxes and triggers Autonomous Emergency Braking (AEB).
autovision_adas_demo.ipynbOpenCV & 77 GHz Sensor Fusion# SCN_CV_04: Camera-Radar Extrinsic Sensor Fusion & Lane Curvature
import cv2
import numpy as np
# 1. 3D Radar Track to 2D Camera Image Plane Projection
# Projection: s * [u, v, 1]^T = K * [R | t] * [X_radar, Y_radar, Z_radar, 1]^T
P_radar_homog = np.array([radar_track.x_m, radar_track.y_m, radar_track.z_m, 1.0])
P_cam = np.hstack([calib.rot_radar_to_cam, calib.trans_radar_to_cam]) @ P_radar_homog
if P_cam[2] > 0.5: # Target in forward hemisphere
pixel_homog = calib.camera_matrix @ P_cam
u_proj = int(pixel_homog[0] / pixel_homog[2])
v_proj = int(pixel_homog[1] / pixel_homog[2])
# 2. Spatial Bounding Box Association & Fused TTC
for obs in detected_obstacles:
x, y, w, h = obs.bbox
if x <= u_proj <= x + w and y <= v_proj <= y + h:
obs.radar_fused = True
obs.radar_range_m = float(np.linalg.norm([radar_track.x_m, radar_track.y_m, radar_track.z_m]))
obs.radar_doppler_mps = radar_track.doppler_mps
# Doppler-stabilized Time-to-Collision
obs.ttc_sec = obs.radar_range_m / max(0.1, abs(obs.radar_doppler_mps))
# 3. Bird's-Eye View Curvature Radius & Lateral Offset
R_curve_m = ((1 + (2*left_fit_cr[0]*y_eval + left_fit_cr[1])**2)**1.5) / abs(2*left_fit_cr[0])
vehicle_offset_m = (lane_center_px - (image_width / 2.0)) * xm_per_pixSystem Architecture & Engineering Metrics (PROP-OPENCV-ADAS-001)
- Target Frame Rate
- ≥ 30 FPS continuous processing on 1080p/720p automotive camera streams (latency ≤ 33 ms)
- Computer Vision Framework
- OpenCV 4.x (C++17 production pipeline & Python 3.10 research testbench) with Eigen3 linear algebra
- Deep Learning Engine
- OpenCV DNN module (cv::dnn::readNetFromONNX) running YOLOv8-Nano with FP16 / AVX2 acceleration
- Lane Geometry Engine
- Inverse Perspective Mapping (IPM) + Sliding Window Histogram + 2nd-Order Polynomial Fit
- Distance & TTC Methods
- Pinhole geometry Z = f_y·H/h, Lucas-Kanade optical flow expansion, and Doppler direct measurement
- Radar Interoperability
- Extrinsic spatial fusion with 77 GHz FMCW Radar Multi-Target Tracker (SPEC-SW-RADAR-TRK-001)
- ADAS Safety Functions
- Lane Departure Warning (LDW), Forward Collision Warning (FCW < 2.6s), and AEB (< 1.4s / 0.8s)
- Visual Telemetry
- Augmented Reality Head-Up Display (HUD) overlay with real-time 2D Bird's-Eye View radar inset
Comprehensive Automotive Perception: Vision + Radar Synergy
Modern automotive safety standards (Euro NCAP 5-star ratings) mandate redundant, cross-verifying sensor modalities. AutoVision-ADAS provides the optical perception stack (semantics, road boundaries, classification), while the companion 77 GHz FMCW Radar Tracker provides all-weather Doppler velocity and centimeter-accurate ranging. Together, they demonstrate an end-to-end perception and sensor fusion capability for Level 2+ / Level 3 autonomous driving.
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