About Helm.ai

Helm.ai develops full-stack AI software for advanced driver assistance systems (ADAS) and Level 4 autonomous driving, utilizing deep neural networks and generative AI for real-time perception and intent prediction. Their technology reduces development costs and accelerates time to market by providing scalable training and validation tools for automakers facing complex driving conditions.

```xml <problem> Developing and validating advanced driver-assistance systems (ADAS) and Level 4 autonomous driving software requires extensive, high-quality training data that accurately reflects diverse and challenging real-world driving conditions. Generating this data through real-world driving is costly, time-consuming, and limited by the availability of edge cases and rare scenarios. </problem> <solution> Helm.ai offers a full-stack AI software suite and AI-powered tools designed to streamline the development and validation of ADAS and autonomous driving systems. Their core technology leverages deep neural networks (DNNs), Deep Teaching, and generative AI to enable scalable training and validation. The platform includes on-vehicle perception and intent prediction software, as well as offline foundation models for AI-based training and validation. These tools allow automakers to accelerate time to market, reduce development costs, and differentiate their offerings through robust and reliable autonomous driving capabilities. </solution> <features> - Production-grade perception software for single cameras, surround-view, and multi-sensor fusion systems - DNN-based intent and path prediction models for human-like driving maneuvers - GenSim-2: Generative AI tool for augmenting real-world videos or creating fully synthetic driving scenarios with accurate labels - Vidgen-2: Generative AI foundation model for producing realistic video driving data - WorldGen-1: Multi-sensor generative AI foundation model for simulating the full autonomous vehicle stack - Deep Teaching: Unsupervised learning approach for training models on large-scale datasets - Capabilities for weather and illumination transfer, road geometry modification, and traffic participant customization </features> <target_audience> The primary target audience includes automotive OEMs and Tier 1 suppliers developing ADAS and Level 4 autonomous driving systems. </target_audience> ```

What does Helm.ai do?

How much funding has Helm.ai raised?

Helm.ai has raised 100530000.

Funding

100530000

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Helm.ai

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Executive Summary

helm.ai LinkedIn Profile

Funding

Estimated Funding

$100M+

Team

No team information available.

Company Description

Problem

Developing and validating advanced driver-assistance systems (ADAS) and Level 4 autonomous driving software requires extensive, high-quality training data that accurately reflects diverse and challenging real-world driving conditions. Generating this data through real-world driving is costly, time-consuming, and limited by the availability of edge cases and rare scenarios.

Solution

Helm.ai offers a full-stack AI software suite and AI-powered tools designed to streamline the development and validation of ADAS and autonomous driving systems. Their core technology leverages deep neural networks (DNNs), Deep Teaching, and generative AI to enable scalable training and validation. The platform includes on-vehicle perception and intent prediction software, as well as offline foundation models for AI-based training and validation. These tools allow automakers to accelerate time to market, reduce development costs, and differentiate their offerings through robust and reliable autonomous driving capabilities.

Features

Production-grade perception software for single cameras, surround-view, and multi-sensor fusion systems

DNN-based intent and path prediction models for human-like driving maneuvers

GenSim-2: Generative AI tool for augmenting real-world videos or creating fully synthetic driving scenarios with accurate labels

Vidgen-2: Generative AI foundation model for producing realistic video driving data

WorldGen-1: Multi-sensor generative AI foundation model for simulating the full autonomous vehicle stack

Deep Teaching: Unsupervised learning approach for training models on large-scale datasets

Capabilities for weather and illumination transfer, road geometry modification, and traffic participant customization

Target Audience

The primary target audience includes automotive OEMs and Tier 1 suppliers developing ADAS and Level 4 autonomous driving systems.