  Achieving robust and real-time 3D perception is fundamental for autonomous
vehicles. While most existing 3D perception methods prioritize detection
accuracy, they often overlook critical aspects such as computational
efficiency, onboard chip deployment friendliness, resilience to sensor mounting
deviations, and adaptability to various vehicle types. To address these
challenges, we present NVAutoNet: a specialized Bird's-Eye-View (BEV)
perception network tailored explicitly for automated vehicles. NVAutoNet takes
synchronized camera images as input and predicts 3D signals like obstacles,
freespaces, and parking spaces. The core of NVAutoNet's architecture (image and
BEV backbones) relies on efficient convolutional networks, optimized for high
performance using TensorRT. More importantly, our image-to-BEV transformation
employs simple linear layers and BEV look-up tables, ensuring rapid inference
speed. Trained on an extensive proprietary dataset, NVAutoNet consistently
achieves elevated perception accuracy, operating remarkably at 53 frames per
second on the NVIDIA DRIVE Orin SoC. Notably, NVAutoNet demonstrates resilience
to sensor mounting deviations arising from diverse car models. Moreover,
NVAutoNet excels in adapting to varied vehicle types, facilitated by
inexpensive model fine-tuning procedures that expedite compatibility
adjustments.
