Driving Simulation Scene Generation for Smooth Background Traffic
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Solution Overview
Problem
Existing driving simulation platforms for autonomous vehicles require manual input for constructing large-scale simulative test scenarios, leading to low processing efficiency due to the need for manual addition of background vehicles.
Innovation Solution
A method and apparatus for automatically constructing a driving simulation scenario by determining route endpoints, generating test and background vehicles based on schedules, and controlling their movement within a road network model, ensuring efficient generation and disappearance of vehicles to avoid abrupt display effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual addition of background vehicles is used to construct simulative test scenario, then the simulation scenario can be constructed, but the processing efficiency is low
Solution Approach 1:
The system automatically generates background vehicles based on predefined schedules and road network models without requiring manual intervention. The generation unit autonomously determines vehicle types, positions, and routes, enabling the simulation platform to serve itself in constructing test scenarios.
Solution Approach 2:
The system uses configurable parameters such as vehicle generation schedules, traffic flow rates, and spatial distribution coefficients to dynamically adjust the automatic generation process. By modifying these parameters, the system can adapt to different simulation requirements while maintaining automated operation.
2Quantity of substance
If large quantity of background vehicles are added to create large-scale simulative test scenario, then the simulation coverage is improved, but the processing time increases
Solution Approach 1:
The system pre-defines vehicle generation schedules, routes, and distribution patterns before simulation execution. By preparing these configurations in advance, the system can rapidly generate large numbers of background vehicles during simulation without incurring additional construction time.
Solution Approach 2:
The system uses template-based vehicle models and reusable route configurations that can be copied and instantiated multiple times. This allows rapid proliferation of background vehicles from standardized templates, significantly reducing the time required to create large-scale scenarios.
3Reliability
If background vehicles are generated and disappeared abruptly in simulation, then the simulation can proceed, but visual errors and traffic status abruptness occur
Solution Approach 1:
The system dynamically adjusts vehicle generation and removal based on real-time simulation conditions, traffic flow requirements, and spatial distribution. Vehicles are generated and removed smoothly over time rather than abruptly, maintaining continuous and realistic traffic status throughout the simulation.
Solution Approach 2:
The system monitors traffic flow density and vehicle distribution during simulation, using this feedback to regulate the generation and removal of background vehicles. This ensures that vehicles are added or removed only when appropriate conditions are met, preventing visual errors and maintaining simulation reliability.
Data Source
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AI summary
Provided is a processing method for a driving simulation scene. The method comprises: determining a plurality of path endpoints in a road network model for driving simulation (101); selecting at least one path endpoint from among the plurality of path endpoints as a departure point, and selecting at least one path endpoint as a vehicle return point (102); and generating a background vehicle at the departure point in the road network model, controlling the background vehicle to drive into the road network model from the departure point, and controlling the background vehicle to drive out of the road network model when the background vehicle drives to the vehicle return point (103). Further disclosed is a driving simulation apparatus, comprising: an acquisition unit for acquiring a plurality of path endpoints in a road network model for driving simulation; a determination unit for determining at least one path endpoint in the plurality of path endpoints as a departure point and at least one path endpoint as a vehicle return point; a generation unit for generating a background vehicle at the departure point in the road network model; and a control unit for controlling the background vehicle to drive into the road network model from the departure point, and controlling the background vehicle to drive out of the road network model when the background vehicle drives to the vehicle return point. Further disclosed is a storage medium for implementing the processing method for a driving simulation scene. According to the present invention, a driving simulation scene for vehicle simulation can be automatically constructed, and the processing efficiency is high.