Recommended Actions:
Conduct business model assessment of the short- and long-term impact of autonomous vehicles’ transition away from driver-risk-led underwriting on your solution portfolio. Prepare to offer twin products that facilitate both driver risk and technology risk during this transition period to maximize revenue potential. ■ Position new solutions toward market opportunities where there is lower complexity and no regulatory hindrance. Examples include private road networks of either commercial entities (like ports, mines) or communities (like private residential estates). ■
Recommended Reading:
Emerging Tech Impact Radar: Autonomous Vehicles, 2022 ■
Hype Cycle for Connected, Electric and Autonomous Vehicles, 2023 ■
Cool Vendors in Perception Solutions for Autonomous Vehicles ■
Market Trend: Connected and Autonomous Vehicle Data Enhances Software Life Cycle Management Transformation ■
Image and Video Synthetic Data Back to Top
Analysis by: James Ingham, Vibha Chitkara, Alys Woodward, Benjamin Jury
Description: Synthetic data involves data generated artificially instead of from real operations. Image and video synthetic data can be built using generative adversarial networks (GANs) and simulation environments using graphical engines. Synthetic data GANs techniques are mostly used to enhance and/or increase the dataset available from the real world, while simulation techniques add new and original human knowledge of physics objects and rules. Synthetic data generation capabilities can address many specific AI and analytics concerns, such as data scarcity, accessibility, bias, privacy and regulatory compliance by supplementing real data. Image synthetic data of particular types of objects has its own characteristics, and vendors often focus on selections of image types — for example, human images, object images, indoor locations, outdoor locations or geographic images.
Gartner, Inc. | G00786204
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