Blog / The engineering notebook
Ideas tested.
Lessons shared.
Field notes from building AI systems. Practical experiments, engineering decisions, and lessons worth sharing.
Blog / The engineering notebook
Field notes from building AI systems. Practical experiments, engineering decisions, and lessons worth sharing.
Lesson 4: turning camera, LiDAR, and radar data into objects. Classification, detection, and segmentation, what each sensor is good and bad at, and why Apollo fuses them rather than trusting one.
Lesson 3: how the vehicle places itself to single-digit-centimetre accuracy. Inertial navigation, GNSS and IMU, RTK, LiDAR and visual localization, and the multi-sensor fusion Apollo settles on.
Lesson 2: why a self-driving car needs an HD map rather than the one on your phone. Centimetre precision, lane markings and a 3D road network, how localization rides on top of the map, and how the maps get built.
Lesson 1: what actually makes up a self-driving car. The six levels of autonomy, how a machine driver differs from a human one, and the three layers of the Apollo platform: hardware, the open software stack, and the cloud services.
Lovász-Softmax is a differentiable surrogate for the Jaccard/IoU loss. It sorts pixel errors and weights them by the discrete change they cause in IoU.
The three deep networks behind my Jetson Nano ADAS: CenterNet detection, a ResNet-18 sign classifier, U-Net lane segmentation, plus TensorRT tuning.
Software design of an ADAS on Jetson Nano: forward collision warning, lane departure detection, and speed-limit sign over-speed alerts.
Building an ADAS prototype on a Jetson Nano: the motivation, feature scope, and hardware design for collision, lane-departure, and speed-limit warnings.
CBHG is the sequence-processing block used in the original Tacotron: a 1D convolution bank, highway network, and bidirectional GRU working at different context scales.
VGG perceptual loss compares generated and target images in a frozen feature space instead of requiring every output pixel to match.
Set an Intel integrated GPU as the display renderer on Ubuntu 18.04 so your NVIDIA GPU's full VRAM stays free for CUDA and model training.
Accuracy alone hides a bad classifier. A walkthrough of the confusion matrix and what is built from it: precision, recall, specificity, F-score, and the AUC-ROC curve, with when each one is the metric that matters.