Work-in-Progress: Context and Noise Aware Resilience for Autonomous Driving Applications

Autonomous Vehicles (AVs) often use noise prone sensory data from cameras and LiDAR for perception. In specific noisy scenarios, different object detection models exhibit non-intuitive and varying degrees of resilience, necessitating adaptive model selection. In this work, we develop a context and n...

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Bibliographic Details
Main Authors: Alikhani, Hamidreza, Kanduri, Anil, Liljeberg, Pasi, Rahmani, Amir M., Dutt, Nikil
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Autonomous vehicles
Context aware ML
Context modeling
Data models
Efficient Inference
Laser radar
Low latency communication
Noise
Noise measurement
Object detection
Resilience
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Summary:Autonomous Vehicles (AVs) often use noise prone sensory data from cameras and LiDAR for perception. In specific noisy scenarios, different object detection models exhibit non-intuitive and varying degrees of resilience, necessitating adaptive model selection. In this work, we develop a context and noise aware framework for run-time adaptive configuration of objection models for high accuracy and low latency inference. We combine driving scene context and input data noise to prioritize among input modalities, followed by selection and configuration of most resilient object detection model appropriate for the context. Our evaluation for 2D object detection on nuScenes dataset provided average 1.83x speedup in latency compared to baseline while preserving average prediction confidence.
ISSN:2832-6474
DOI:10.1109/CODES-ISSS60120.2024.00010