Presentation
Scalable Framework for Traffic Rule Enforcement in Autonomous Driving: Evaluating Adaptability Across Edge Platforms
DescriptionThis paper presents a scalable framework for enforcing traffic regulations in autonomous driving, integrating CARLA simulations with edge deployments on Raspberry Pi 5 and Jetson Nano p3541. Fine-tuned language models, including GPT-2, GPT-Neo, OPT, and BLOOM, classify driving behaviors. Results highlight platform-specific trade-offs, with Raspberry Pi achieving consistent performance for smaller models in 5–10 seconds, while Jetson Nano, despite CUDA issues, showed faster processing for BLOOM and OPT, reducing detection times by up to 20%. The framework emphasizes adaptability in edge-based compliance monitoring and opportunities for optimization in resource-constrained environments.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby