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ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency
Deep neural networks (DNNs) are increasingly integrated into LiDAR (Light Detection and Ranging)-based perception systems for autonomous vehicles (AVs), requiring robust performance under adversarial conditions.
DynaMIX: Resource Optimization for DNN-Based Real-Time Applications on a Multi-Tasking System
As deep neural networks (DNNs) prove their importance and feasibility, more and more DNN-based apps, such as detection and classification of objects, have been developed and deployed on autonomous vehicles (AVs).
Data-free mixed-precision quantization using novel sensitivity metric
Post-training quantization is a representative technique for compressing neural networks, making them smaller and more efficient for deployment on edge devices.
Contextual Relationship-based Activity Segmentation on an Event Stream in the IoT Environment with Multi-user Activities
To discover the intrinsic relationships between the events in a stream, we utilized the LSTM model by rendering it for the activity segmentation.
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