Vehicle Performance Profiles for Realistic Driving Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current driving simulators rely on idealized specifications and generic vehicle models, failing to accurately replicate real vehicle behavior, which can lead to inaccurate training and testing of autonomous vehicles as they age or experience different environmental conditions.
Innovation Solution
The system captures real vehicle driving performance data over time to generate a vehicle driving profile, which is then imported into a driving simulator to customize vehicle physics performance, allowing for realistic and immersive training experiences and improved AI policies for autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If idealized specifications and generic vehicle models are used in driving simulators, then the device complexity is reduced and ease of manufacture is improved, but the measurement precision of vehicle behavior replication deteriorates
Solution Approach 1:
The patent creates a digital copy of the vehicle's actual performance characteristics by capturing real vehicle data through sensors and processing it into a vehicle performance profile. This profile is then imported into the simulator to replace generic models, allowing the simulator to replicate the specific vehicle's behavior accurately without requiring physical modifications to the simulator hardware.
Solution Approach 2:
The system changes the parameters of the simulated vehicle by importing real performance data that modifies mass, drag coefficient, rolling resistance, and other physical parameters. This transforms the simulator from using fixed generic parameters to using dynamically adjustable parameters based on actual vehicle measurements, improving replication accuracy while maintaining software-based flexibility.
2Reliability
If generic vehicle models are used in driving simulators, then the adaptability to different vehicle types is improved, but the reliability of autonomous vehicle training deteriorates
Solution Approach 1:
The system makes the vehicle model dynamic by allowing parameters to be updated based on the actual vehicle being tested. Instead of using static generic models, the simulator dynamically adjusts mass, aerodynamic properties, and mechanical characteristics to match the specific vehicle, ensuring reliable training data while maintaining the ability to adapt to different vehicle types through software configuration.
3Measurement precision
If real vehicle performance data is captured and imported into the simulator, then the measurement precision of vehicle behavior is improved, but the loss of time for data collection and processing increases
Solution Approach 1:
The system performs preliminary data collection and processing by capturing vehicle performance data in advance through sensors and processing it into a ready-to-import profile format. This preliminary action allows the actual simulator setup to be faster, as the data preparation work is completed beforehand rather than during the simulation configuration process.
Data Source
AI summary
Systems and techniques for simulated vehicle operation modeling with real vehicle profiles are described herein. In an example, a simulated vehicle modeling system is adapted to obtain a vehicle performance fingerprint, such as from a vehicle performance fingerprint that includes vehicle performance data collected from a unique vehicle while experiencing real world driving conditions. The simulated vehicle modeling system may be further adapted to present, in a driving simulator, a simulated driving experience with a simulated vehicle, such as for a simulated vehicle that is constructed based on the vehicle performance fingerprint. The simulated vehicle modeling system may be further adapted to update a set of driving directives for an autonomous vehicle based on the simulated driving experience and upload the set of driving directive to an autonomous vehicle.