Autonomous Vehicle Driving Emulation on a Movable Terrain Platform
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Solution Overview
Problem
Current virtual training systems for autonomous vehicles face challenges in emulating the actual movement of vehicles, particularly on uphill, downhill, tilted roads, and uneven surfaces, which are difficult to replicate in a simulated environment.
Innovation Solution
A method and system that utilize a movable platform with processors and sensors to initialize and control the driving environment data, receiving control data from sensors to direct the movement of the autonomous vehicle, simulating real-world driving scenarios such as speed, direction, and terrain variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual training environment is used for autonomous vehicle, then training cost and time are reduced, but the ability to emulate actual movement on complex terrains is worsened
Solution Approach 1:
The system employs a movable platform that can dynamically change its motion state to match the virtual driving operations. The platform transitions from a static training setup to a dynamic one that actively responds to vehicle operations, enabling realistic emulation of movement on various terrains while maintaining virtual training efficiency.
Solution Approach 2:
The movable platform acts as an intermediary between the virtual training environment and the autonomous vehicle. It bridges the gap by physically simulating terrain effects (uphill, downhill, uneven surfaces) that would otherwise be difficult to replicate in a purely virtual environment, thereby improving emulation accuracy without sacrificing training efficiency.
2Reliability
If movable platform is introduced to emulate actual movement, then emulation accuracy is improved, but system complexity is worsened
Solution Approach 1:
The movable platform is designed to perform multiple functions: it can simulate various terrains (uphill, downhill, uneven surfaces), respond to different driving operations (acceleration, braking, turning), and adapt to diverse virtual environments. This multi-functionality reduces the need for multiple specialized devices, thereby managing system complexity while maintaining high emulation accuracy.
Solution Approach 2:
The system implements feedback mechanisms where the autonomous vehicle's driving operations are detected and used to control the movable platform's motion state. This closed-loop feedback system automates the platform's response, reducing the need for complex manual control systems and operators, thus managing complexity while achieving accurate emulation.
3Object-affected harmful factors
If virtual training is used instead of real-world testing, then safety is improved, but the realism of driving conditions is worsened
Solution Approach 1:
The movable platform dynamically adjusts its motion to create realistic driving conditions within the safe confines of a controlled environment. By actively responding to the vehicle's operations and simulating various terrains, it provides realistic sensory feedback to the autonomous vehicle while eliminating the dangers of real-world testing.
Data Source
AI summary
A method and a system for simulating driving operation of the autonomous vehicle are described. The method comprising initializing driving environment data using navigation information associated with the autonomous vehicle, and receiving control data from one or more sensors, wherein the control data affects driving operation of the autonomous vehicle. The method further comprises controlling the driving operation of the autonomous vehicle based on the driving environment data and the control data received, and directing movement of the movable platform on which the autonomous vehicle is placed, based on the driving operation of the vehicle controlled to emulate the movement of the autonomous vehicle.