Cloud-Enabled Ota Deployment: Streamlining Software Updates Across Vehicle Fleets With Ai & Ml Algorithms

Abstract

Deployment of software updates in automotive vehicles is a complex, error-prone, and tedious process compared to conventional consumer electronics. The process has its punitive challenges and must account for several contributing factors, such as consumer safety, privacy, regulatory compliance, resource constraints, and security.Today, infrequent, disengaging, and decentralized dealer-centric software updates put vehicles at risk of not being in the latest software state. The dealer-centric updates introduce laborious management, increased resources, and higher cost overhead for the OEMs. Additionally, quality challenges and diverse vehicle populations further complicate the over-the-air (OTA) deployment process.Moreover, the management complexity, resource constraints, and urgency to quickly respond to newly detected issues are far beyond the scope of the dealer network and usually converge on the vehicle OEMs. Expensive recalls, subsidizing a dealer facility with flash facility tools, and/or financing a third party for field service are not viable monetizable strategies nor represent proactive capacity management for such infrequent and high-touch operational process aspects that surround vehicle software updates.By utilizing the cloud, AI and ML technologies, and infotainment capabilities of the vehicle, vehicle population, and the vehicle on the road aspects combined with the surgeon capability of the vehicle, not only can the operational labor complexities of conducting dealer facility updates be significantly reduced, but proactive vehicle updates can be reached in a near-seamless and unattended manner. The "broad-tailed" vehicle adoption over its lifetime life-cycle results in a cost-effective, operational overhead reduction, seamless alerts, warnings, and reminders, all of which represent the key "smart" vehicle aspects that are capable of driving customer satisfaction.In this paper, we share a brief perspective on how the OTA deployment challenges are being addressed to drive modern software management approaches for current and future electronics solutions within the vehicle domain. We provide a data-driven approach with descriptive analytics for model generation via advanced learning techniques, and deep neural network architectures that ultimately close the gap to the desired state. Our cloud-enabled AI/ML algorithms culminate this paper, thus providing a smooth pathway to reach the modern software update (i.e., fresh condition) within the vehicle electronic domain. We also present different options for market applications and possibilities such as data-driven model deployments on the cloud infrastructure that provide business continuity, customer service, and operational readiness at all times.