Compress Your Development Roadmap

Exceptional AI

Scenarios - AutoLabeling - MLOps - Regression Testing

MLOps Architecture

A full end to end pipeline to accelerate your ADAS model development and a catalyst for your autonomy program to expedite ODD expansion and Map expansion.

Perception Vs Reality

The next generation of AI developers will differentiate themselves based on their access to scenario datasets and tooling. Having precise ground truth labeled data is paramount to solving for edge cases and corner cases. Auto-labeling the scenario data is the holy grail of training ML models.

Collect Data

Upload, index and tag interesting scenarios


Autolabel the scenario data to prepare dataset

Review Data

Augment Autolabeling with Ground Truth Tools

Train ML

Regressively test your scenarios for ML Training

Explore our Sample Dataset

RoadMentor is a cutting-edge MLOps platform designed specifically for Advanced Driver Assistance Systems (ADAS) vehicles. It provides a centralized platform for managing and deploying machine learning models in ADAS vehicles, enabling efficient and streamlined development, testing, and deployment processes. The platform enables organizations to quickly and easily deploy and manage machine learning models for autonomous vehicles, reducing the time and cost associated with developing, testing, and deploying models.

This dataset provides a comprehensive overview of the platform’s capabilities, including real-time data from ADAS vehicles, as well as results from various machine learning models. The sample dataset is a great tool for developers and data scientists to gain a deeper understanding of the platform and its capabilities.


Watch Demo


Bridge the Gap Between
Perception & Reality 
With Our Tools

Autonomy today suffers from a series of interrupts causing a bad user experience. These interrupts are situations which are difficult to discern for a software but easy for humans. As limited autonomy deployments scale to multi-city production operations, there is a substantial rise in need to understand, classify and measure the reality. Perception is not always reality and the same principle applies to self driving vehicles. What they perceive might not reflect facts on the ground. So how can a system self correct if it doesn’t know the difference?


Prior Work

Optimize your ML workflows with advanced ground truthing and error analysis. Join RoadMentor Accelerator Program today.

Synthetic Dataset

Sensor Fusion Validation

San Francisco, CA

GNSS Error

Your Dataset

Sensor Calibration + Error Analysis

Stay Up To date

Keep up with our latest updates and news.

Sensor Calibration Service using Fisheye GL and LiDAR Projection Mapping with HTML5 Canvas and JavaScript

Introduction: Sensor calibration is essential for accurate data interpretation and fusion in various applications, such as autonomous vehicles, robotics, and remote sensing. This blog post will provide a step-by-step guide[…]

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RoadMentor Demo Day March 7

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Orchestrating ML Success on Your Own Terms: The Benefits of RoadMentor’s Customer Infrastructure

The RoadMentor platform is an innovative machine learning operations (MLOps) platform specifically designed for advanced driver assistance systems (ADAS). This platform leverages the latest in infrastructure orchestration technologies to provide[…]

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The Benefits of a Unified Product Experience

The market for machine learning algorithms for autonomous driving is still relatively young and, as a result, remains highly fragmented. With so many companies and organizations developing their own unique[…]

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Navigating the World with Words: How Large Language Models Power Autonomous Systems

HD maps are a critical component of autonomous vehicle technology, providing information on the physical environment in which the vehicle operates. The semantic layer of an HD map is a[…]

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How to conquer distortion and converge towards higher quality sensor fusion.

Distortion is an important factor to consider in image feature tracking, image stitching, point cloud registration, and fusing data from multi-modal sensors. Distortion can occur due to various factors such[…]

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How to stitch multiple cameras together on a moving vehicle

Image stitching is the process of combining multiple images together to create a seamless panorama or a large-scale image. This technique is commonly used in photography, virtual reality, and mapping[…]

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Down in the trenches with Robert, a Geospatial Ops Manager for a large ADAS team

As a Geospatial Ops Manager, Robert is responsible for running product operations for a large Advanced Driver Assistance Systems (ADAS) team at an Original Equipment Manufacturer (OEM). This includes managing[…]

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How to use The Potential of Unsupervised Auto-Labeling: A Guide to Building Scalable MLOps Pipelines

Building a scalable MLOps pipeline for unsupervised machine learning can be a challenging task for engineers, but with the right approach, it can be done efficiently. One key aspect of[…]

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Unlock the Power of Vision Analytics with Fleet Queries: A Guide to Optimizing Your ADAS Fleet

Advanced Driver Assistance Systems (ADAS) are becoming increasingly prevalent in today’s vehicles, and with that comes the need for efficient and effective data management. One powerful tool for managing data[…]

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Revolutionizing L3 ADAS: Harnessing the Power of Test Harnesses and MLOps for Safe and Reliable Automotive Technology

Regression testing is a crucial aspect of developing L3 Advanced Driver Assistance Systems (ADAS) technology. It involves re-testing the entire system or parts of it, after changes or modifications have[…]

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What is ADAS (Level 2 and below)?

Advanced Driver Assistance System (ADAS) is a technology that helps drivers to drive safely and efficiently. It is a combination of sensors, cameras, and other technologies that work together to[…]

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Revolutionizing the Road: How Hyperspec AI’s Fleet Learning Approach is Changing the Game for Detecting Lane Merges on the Highway

Automating the process of data annotation has been a long-standing challenge in the field of machine learning, especially when it comes to detecting lane merges on the highway. The traditional[…]

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Collaboration vs Consolidation: Navigating the Evolving Autonomous Driving Industry

The evolution of the Autonomous Driving (AD) industry has been rapid and dynamic, As the industry has grown, we have seen a wide variety of approaches to the development of[…]

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Navigating the Road to Success: How Abstracting Maps for Self-Driving Cars Ensures Safe and Efficient Operation

Self-driving cars rely heavily on maps to navigate and make decisions while on the road. These maps must be accurate, detailed, and up-to-date in order for the car to operate[…]

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Uncovering ADAS Failure Cases: The Importance of Edge Vision Analytics and Fleet Queries in Vehicle Safety

Collecting failure cases from a fleet of L3 ADAS vehicles is a crucial task in ensuring the safety and reliability of these systems. With the increasing use of advanced driver[…]

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Drive smarter, not harder: The OODA loop’s way to advanced driving assistance

The OODA loop, or Observe, Orient, Decide, and Act loop, is a decision-making framework that was developed by military strategist and United States Air Force Colonel John Boyd. The OODA[…]

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Navigating the complexities of coordinate frames: A guide to understanding the differences in Three.js, ROS, and Unreal Engine

Coordinate frames are an important aspect of robotics and computer graphics, as they determine the position and orientation of objects in 3D space. However, different platforms and software libraries use[…]

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Jane, a superstar ADAS engineer

As the sun rose over the bustling city, Jane, an Adas engineer at a leading autonomous vehicle company, woke up to the familiar sound of her alarm clock. She knew[…]

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Unsupervised Machine Learning and Speed Ups in Labeling

Unsupervised machine learning is a type of machine learning that involves training a model on a dataset without providing it with labeled examples. Instead, the model is asked to discover[…]

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The Purpose of the Map

There’s a scene from The Office (American) where Michael so literally follows the instructions from his GPS that he drives into the lake while screaming ‘THE MACHINE KNOWS’. While it[…]

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Javascript, Caching to Optimize Network Traffic

Browsers use caching to temporarily store resources locally on the device, such as HTML, CSS, and JavaScript files, images, and videos. This allows the browser to quickly access the resources[…]

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Training PackNet-SFM with RGB + PointCloud Data

Fusing time synchronized RGB camera images with point cloud data is a powerful approach for training a PackNet-SFM (Structure from Motion) model in PyTorch. This technique combines the benefits of[…]

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Roadmap Compression via Streamlining MLOps Infrastructure and its Impacts on ADAS programs

Advanced Driver Assistance Systems (ADAS) are becoming increasingly important in the automotive industry as they offer a wide range of features that enhance the safety and comfort of the driving[…]

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Painless Ground Truth Data

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Our team

Sravan Puttagunta


Emily Leung

CRO, Head of Partnerships

Srinayani Vardhaman

Senior GIS Analyst

Venkata Kolla

VP of Engineering

Sean Liu

Sr. Lead Engineer (Algorithms)

Rui Chen

Senior Engineer (Synthetics)

Saket Sonekar

Sr. QA Manager(Ground Truthing)

Abhijeet Sran

Director of Product Operations