Advanced Webinar Invitation, and A New Way to Test and Validate Autonomous Vehicles

Introducing: Autonomous Driving Validation Library - A New Way to Test and Validate Autonomous Vehicles

As the world moves towards a future of autonomous vehicles (AVs), it becomes increasingly important to ensure that these vehicles are safe and efficient. To achieve this, commercial companies and researchers worldwide are developing new technologies and methods to test and validate AVs. One such solution is Cognata’s Autonomous Driving Validation Library (ADVL), a combination of a scenario-based simulation engine and a comprehensive library of scenarios aimed at testing and validating AVs through standard packages of different driving scenarios.

ADVL building blocks logic starts from basic scenarios that test, for example, lane keeping during a bend, Automatic Lane Keeping System (ALKS) or Electronic Stability Control (ESC), or avoiding different types of safety hazards on the road, such as Emergency Brake Assist (EBA). These scenarios then gradually progress to test the AV in more challenging and complex situations and events, such as Lane Support Systems (LSS) or Adaptive Cruise Control (ACC). The ADVL library is based on growing worldwide research that aims to develop safe and effective autonomous driving, promote AV validation, and enable AV regulation.

Check out the demo video below:

Other companies are also investing in the development of AV technology and testing methods. Some have built testing facilities, where they test AVs in a range of scenarios, including simulated city environments and challenging weather conditions. Some rely on real-world data collected from their customers’ vehicles to continuously improve their Autopilot system. Companies and consortiums alike have been testing AVs in multiple cities across the world, progressing the autonomous vehicle ambition.

Join us for the upcoming webinar!

Advanced Techniques for Bridging AV Data Gaps – Applied Explainability and Virtualized Sensor Simulation

Constructing high-quality datasets that are diverse, realistic, and extensive enough to train autonomous and ADAS sensors is crucial for ensuring reliable performance in complex and unpredictable driving scenarios where lives are at risk.

Today, developing deep-learning models for these safety systems and sensor models requires levels of accuracy that are virtually impossible to achieve using current development paradigms.

In this informative webinar, experts from Tensorleap , Cognata, and Foresight Automotive will discuss ways to overcome the complexities of developing automotive AI, resolving model weaknesses, and pinpointing the data you need.

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Key topics and takeaways:

• Learn about the challenges of generating synthetic data and how to create balanced, high-quality datasets with data-driven insights
• Discover how to pinpoint and resolve edge cases before failures in production
• Find out how to use innovative applied explainability techniques to overcome critical dataset construction issues and pinpoint and bridge domain gaps in minimal time
• Understand how to optimize your sensor suite virtualization and simulation strategies to meet your organization’s needs