GenAI on the Edge Forum: Generative AI on the Edge for Connected Vehicles and Mobility
Generative AI on the Edge for Connected Vehicles and Mobility
Alok RANJAN, Research Architect, Bosch
Background:
Generative Artificial Intelligence (GenAI) is disrupting the technology landscape of several industries and many research groups have picked up this fast-evolving trend and demonstrated the impact and capability of GenAI to unlock new values. From texts to images, audio, video, new content/design generation to synthetic data; it has been extensively explored by the early adopters. Although the solution like ChatGPT has been a household name, GenAI adoption in different industry domains beyond custom chatbots and automation is still under progression phase due to obvious questions on security and privacy.
Most recently, automotive industry is going through the transformation journey in the direction of PACE (Personalization, Autonomous, Connected and Electrified) and AI based services are further fueling these technical advancements. From Passenger vehicles to commercial vehicles, there are now more advanced sensors generating high volume data which is further leveraged to offer connected services. Furthermore, in-vehicle connected features and personalization are moving towards more advancements and new features integration are on high demand. With the advent of GenAI and its capabilities, it is now feasible to offer hyper personalization features to the customers which shall increase customer experience to next level.
Thrust areas:
Although the recent advancements in GenAI domain have been discussed by the community, it is worth to mention that majority of applications including training/retraining to finetuning is majorly cloud native. As we know, traditional cloud-based architecture is limited by network bandwidth, data privacy, latency etc., which could be addressed using technologies like Edge Artificial Intelligence (Edge AI) and TinyML. Edge AI has been realized in certain use cases for the domain of connected vehicles such as object detection and classification, prognosis, gesture recognition with control and many others. GenAI particularly on the edge within the vehicular systems or connected vehicles is yet to be them mainstream for automotive industry. This is motivated from the fact that the current architecture, optimizations strategies and finetuning on custom business data need research advancements from the edge ecosystem perspectives.
In this presentation, we will discuss the best practices and some most recent hybrid edge-cloud architectures which have been realized to bring GenAI on the edge from connected vehicles and mobility perspectives. In particular, we will first present the specific use cases and how GenAI is helpful to offer hyper personalization services. We then discuss the advantages and benefits considering edge ecosystem. Future research directions where the community could help in advancing the domain from vehicular ecosystem will be also presented considering topics like specialized hardware architectures, optimization strategies, privacy preserving techniques such as edge federated learning in hybrid architecture.
source
