Real-Time Visual Perception Acceleration for Autonomous Driving Based on TensorRT Quantization and Mixed-Precision Inference

Tianlang Xin

doi:10.47297/taposatWSP2633-456934.20260708

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Real-Time Visual Perception Acceleration for Autonomous Driving Based on TensorRT Quantization and Mixed-Precision Inference

7卷第8期

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Real-Time Visual Perception Acceleration for Autonomous Driving Based on TensorRT Quantization and Mixed-Precision Inference
Real-Time Visual Perception Acceleration for Autonomous Driving Based on TensorRT Quantization and Mixed-Precision Inference
Electrical & Electronic Engineering, Xiamen University Malaysia, Selangor, 43900, Malaysia
2026年7卷第8期页码：172-178
纸质出版：2026-08-30
DOI：10.47297/taposatWSP2633-456934.20260708
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Tianlang Xin. (2026). Real-Time Visual Perception Acceleration for Autonomous Driving Based on TensorRT Quantization and Mixed-Precision Inference. 科技理论与实践（英文版）, 7(8), 172-178.

Xin Tianlang.Real-Time Visual Perception Acceleration for Autonomous Driving Based on TensorRT Quantization and Mixed-Precision Inference[J].Theory and Practice of Science and Technology,2026,07(08):172-178.
Tianlang Xin. (2026). Real-Time Visual Perception Acceleration for Autonomous Driving Based on TensorRT Quantization and Mixed-Precision Inference. 科技理论与实践（英文版）, 7(8), 172-178. DOI： 10.47297/taposatWSP2633-456934.20260708.

Xin Tianlang.Real-Time Visual Perception Acceleration for Autonomous Driving Based on TensorRT Quantization and Mixed-Precision Inference[J].Theory and Practice of Science and Technology,2026,07(08):172-178. DOI： 10.47297/taposatWSP2633-456934.20260708.

摘要

Abstract

Autonomous driving systems require efficient and reliable visual perception models for real-time deployment on resource-constrained platforms. This study proposes a TensorRT-based acceleration framework for YOLOv3 by integrating mixed-precision inference and INT8 quantization to improve deployment efficiency while preserving detection performance. Experiments on the KITTI dataset show that TensorRT optimization can significantly reduce inference cost and resource consumption. FP16 inference maintains stable detection accuracy while improving efficiency

whereas INT8 quantization provides greater acceleration with only limited performance degradation. Overall

the proposed framework achieves a practical balance between accuracy and real-time performance

providing an effective solution for deploying deep learning-based visual perception models in autonomous driving scenarios.

关键词

Keywords

references

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