Ansys, Inc. (20240211647). Systems and Methods for Training and Simulation of Autonomous Driving Systems simplified abstract
Contents
Systems and Methods for Training and Simulation of Autonomous Driving Systems
Organization Name
Inventor(s)
Anupam Ashish of Hallbergmoos (DE)
Evren Yortucboylu of Pucheim (DE)
Systems and Methods for Training and Simulation of Autonomous Driving Systems - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211647 titled 'Systems and Methods for Training and Simulation of Autonomous Driving Systems
Simplified Explanation: The patent application describes a system for simulating the operation of an autonomous vehicle control system using multi-sensor data from real-world drives.
- Three-dimensional multi-sensor data from sensor-equipped vehicles is accessed.
- The data is reduced to a time series of two-dimensional representations for a specific drive.
- The time series is classified into states and stored as a scenario.
- A query can be made to access the scenario based on specific state criteria.
- The data from the scenario is used to simulate the behavior of an autonomous driving system in various scenarios.
Key Features and Innovation:
- Utilizes three-dimensional multi-sensor data for simulating autonomous vehicle control systems.
- Reduces complex data into a time series of two-dimensional representations for analysis.
- Classifies data into states to create scenarios for simulation.
- Allows for querying specific scenarios based on state criteria.
- Enables realistic simulation of autonomous driving system behavior.
Potential Applications:
- Autonomous vehicle testing and development.
- Training simulations for autonomous driving systems.
- Scenario-based training for autonomous vehicle operators.
- Research and analysis of real-world driving data.
- Enhancing safety and efficiency of autonomous vehicles.
Problems Solved:
- Efficiently simulating complex real-world driving scenarios.
- Analyzing and classifying large volumes of multi-sensor data.
- Providing realistic testing environments for autonomous vehicle systems.
- Improving the accuracy and reliability of autonomous driving simulations.
Benefits:
- Enhanced development and testing of autonomous vehicle control systems.
- Improved safety and performance of autonomous vehicles.
- Cost-effective simulation of diverse driving scenarios.
- Accelerated training for autonomous vehicle operators.
- Increased understanding of real-world driving conditions.
Commercial Applications: Autonomous Vehicle Simulation System: Enhancing Development and Testing Efficiencies
Prior Art: Prior research in the field of autonomous vehicle simulation systems and multi-sensor data analysis can provide valuable insights into similar technologies and approaches.
Frequently Updated Research: Stay informed about the latest advancements in autonomous vehicle simulation systems and multi-sensor data analysis to ensure the most up-to-date information on this technology.
Questions about Autonomous Vehicle Simulation Systems: 1. How does this technology improve the safety of autonomous vehicles? 2. What are the potential cost savings associated with using this simulation system for autonomous vehicle development?
Original Abstract Submitted
systems and methods are provided for simulating operation of an autonomous vehicle control system. three dimensional multi-sensor data associated with a plurality of real-world drives in a sensor equipped vehicle is accessed. for a particular drive, the three dimensional multi-sensor data is reduced to a time series of two dimensional representations. the time series of two dimensional representations is classified into a sequence of states, where the sequence of states associated with the particular drive and the three dimensional multi-sensor data are stored in a computer-readable medium as a scenario. a query is received that identifies a state criteria, and the scenario is accessed based on the sequence of states matching the state criteria of the query. the three dimensional multi-sensor data of the scenario is provided to an autonomous driving system to simulate behavior of the autonomous driving system when faced with the scenario.
