Reduce your reliance on expensive and time-consuming real-world testing. Easily validate your system using synthetic maps.
Test your AV system comprehensively. Identify edge cases and areas that rarely occur in the real world.
Ensure the quality of your synthetic maps with pre-built checks, and validate they are error-free and realistic.
Synthetic maps allow for the creation of diverse road networks and environments to test edge cases and rare scenarios that are difficult or dangerous to reproduce in real-world conditions, and ensure ground truth accuracy for comprehensive testing scenarios. These maps also reduce reliance on costly and time-consuming real-world testing by enabling extensive in-simulation validations. Also, synthetic maps can be validated for high-quality and error-free conditions, ensuring the simulations are realistic and effective for training and testing ADAS and AD systems.
Map creation for simulation significantly enhances the accuracy of ADAS and AD testing by providing detailed and controlled environments that replicate real-world scenarios. These synthetic maps enable precise and repeatable tests, ensuring that the systems can be rigorously validated across a variety of conditions and scenarios. This allows developers to identify and address potential issues more effectively, improving the reliability and safety of the driver-assistance and autonomous driving technologies.
Synthetic maps can be designed to include rare or unusual scenarios that may not occur frequently in the real world, allowing developers to identify and resolve potential issues in AD systems in edge cases.
Developers can tailor synthetic maps to specific requirements by manipulating elements such as road configurations, traffic patterns, and environmental conditions, ensuring that the system is tested against the conditions it will likely encounter in the real world.
Creating 3D worlds for sensor simulation offers detailed, dynamic environments that mimic real-world complexity. This detailed environment challenges the perception systems of ADAS and AD systems, training them to recognize and react to varied and unpredictable scenarios. This leads to more robust learning outcomes, better preparing systems to handle real-world driving conditions.