Deepmap is a provider of mapping and simulation technology for self-driving vehicles.  The company offers an end-to-end platform that includes in-vehicle computing and AI infrastructure.

Deepmap

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Vice President Engineering

Andrew Shaw

VP Solution Engineering & Operations

Richard Lucquet

Head of Finance

Qianggen Yang

Human Resources

Cindy Chang

Program Manager

Evgenia Shchemelev

Office Manager in Guangzhou

FAN Rao

Software Engineer

Chirag Majithia

Sensing Systems Engineer

Conner Currie

Engineering

Dengliang Gao

Talent & People Operations

Drew Fong

Michael Cohen

Kevin Fong

Mark Wheeler

Cindy M. K. Chang

Manager, Global Human Resources

Calisa Cole

VP Marketing

Pedro Guillen

Director of Talent

Danny Liu

GM of APAC

Wei Luo

COO & Head of Product

Head of Solution Engineering & Map Operations, VP

Machine Learning Acquired Companies With More Than $1M in Revenue

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DeepMap is accelerating safe autonomy by providing the world's best autonomous mapping and localization solutions. DeepMap delivers the technology necessary for self-driving vehicles to navigate in a complex and unpredictable environment. The company addresses three important elements: precise high-definition (HD) mapping, ultra-accurate real-time localization, and the serving infrastructure to support massive global scaling. DeepMap was founded in 2016 and is headquartered in Palo Alto, Calif., with offices in Beijing and Guangzhou, China. Investors include Andreessen Horowitz, Accel, GSR Ventures, Generation, Goldman Sachs, NVIDIA, and Robert Bosch Venture Capital.

Series B - DeepMap

Bosch Ventures

Accel

Series A - DeepMap

Andreessen Horowitz

Seed Round - DeepMap

Computer and Software Services and Scientific Services

Palo Alto

California

United States

North America

DeepMap

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High definition map based localization optimization

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A system-on-a-chip (SoC) designed for intelligent vehicles, delivering 254 trillion operations per second for autonomous driving capabilities.

A centralized car computer combining advanced driver assistance and in-vehicle infotainment into a single system with AI capabilities.

A reference architecture integrating AI compute with a complete sensor suite for developing, testing, and validating autonomous vehicles.

An operating system for in-vehicle computing, supporting efficient parallel computing and real-time AI inference.

Middleware functions for autonomous vehicle development, including sensor abstraction and deep neural network framework.

An AI-assisted driving platform for handling both highway and urban traffic, providing active safety features and intervention capabilities.

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