Intelligent Voice Activated Drone(s) for in-Vehicle Services and Real-Time Predictions

2021-01-0063

04/06/2021

Features
Event
SAE WCX Digital Summit
Authors Abstract
Content
Today, commercially available drones have limited use-cases in the rapidly evolving community. However, with advances in drone and software technology, it is possible to utilize these aerial machines to solve problems in a variety of industries such as mining, medical, construction, and law enforcement. For example, in order to reduce time of investigation, Indiana State Police are currently utilizing ad-hoc commercial drones to reconstruct crash scenes for insurance and legal purposes. In this paper, we illustrate how to effectively integrate drones for in-vehicle services and real-time prediction for automotive applications. In order to accomplish this, we first integrate simpler controls such as voice-commands to control the drone from the vehicle. Next, we build smart prediction software that monitors vehicle behavior and reacts in real-time to collisions. Furthermore, we employ object recognition techniques through In-Vehicle Infotainment (IVI) systems to identify the surroundings based on inputs from drone-mounted camera sensors. Consequently, we implement object identification and smart maneuver of the drone in relation to the vehicle; as well, employ timely deployment of the drone prior to collision for emergency assistance and crash reconstruction purposes. The goal is to optimize performance and amplify safety and security of the vehicle. The prototype detailed in this paper was tested on a vehicle moving at a speed of 45 mph. The driver of the vehicle can deploy and control the drone using voice commands. The drone follows the vehicle and is in-sync with the vehicle and performs tasks to aid in post-collision assistance and crash reconstruction.
Meta TagsDetails
DOI
https://doi.org/10.4271/2021-01-0063
Pages
9
Citation
Nithiyanantham, M., and Sinnapolu, G., "Intelligent Voice Activated Drone(s) for in-Vehicle Services and Real-Time Predictions," SAE Technical Paper 2021-01-0063, 2021, https://doi.org/10.4271/2021-01-0063.
Additional Details
Publisher
Published
Apr 6, 2021
Product Code
2021-01-0063
Content Type
Technical Paper
Language
English