The Broken Way of Working with Annotation and Perception

The current way of working with annotation and perception in the automotive industry is broken. With a siloed approach and a long chain of processes, it often leads to inefficiency, high costs, and a lack of alignment between engineering needs and delivered data. Let's dive deeper into the challenges and explore how we are revolutionizing this space.

Siloed Work and Long Feedback Cycles

In the industry today, work is often siloed. Engineering teams have specific needs and rely on data procurement to obtain the required data. This sets off a chain reaction where large-scale projects are initiated based on specifications. Annotation vendors then deliver according to these specifications, with procurement acting as the intermediary. The result? A slow and complex process with long feedback cycles.

Due to the lengthy feedback loops, engineers tend to over-specify their requirements, leading to inflated costs and unnecessary delays. This approach lacks flexibility and hinders iterative improvements. It's clear that the current system is inefficient, expensive, and fails to deliver the data that engineering teams truly need.

Our Approach: Accelerating Development Velocity and Lowering Costs

We recognize the flaws of the traditional approach and have developed a platform that addresses these challenges head-on. Our aim is to accelerate development velocity, increase model performance, and lower the total program cost. Let's explore how we achieve this through our innovative approach.

Curation: Maximizing Value and Performance Boosts

We start by focusing on data curation. We understand that data annotation budgets are limited, so we help our customers select the most impactful data to annotate. By identifying the scenarios and scenes that provide the biggest performance boost or are crucial for validation, we ensure that resources are allocated effectively.

Human Feedback Collection: Efficient and Scalable

Collecting human feedback is crucial for improving model performance. We streamline this process by collecting feedback efficiently and at different levels. We enable annotators to provide feedback on the location of objects and the overall driving experience. By making this collection process scalable, we ensure that valuable insights are obtained without sacrificing time or effort.

Data Analysis and Insights: Informed Decision-Making

We understand the importance of analyzing data and gaining insights to make informed decisions. We help ML engineers understand how their models interact with the data and answer critical questions about the data's alignment with specifications. By leveraging insights from the developing model, we ensure that the right data is used and that future data collection efforts are

In conclusion, the current way of working with annotation and perception in the automotive industry is inefficient and costly. However, Kognic is leading the way in transforming this space. Through our platform, we tackle the challenges of a siloed approach, long feedback cycles, and costly iterations. By accelerating development velocity, increasing model performance, and lowering costs, Kognic remains a trusted partner for companies looking to win in the ever-changing landscape of perception.