Autonomous Driving Route Reliability Simulation
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Solution Overview
Problem
Autonomous driving systems face unpredictability due to varying traffic environments, leading to situations where users are forced to perform manual driving, as the reliability of autonomous driving depends on the conditions under which the trained model was trained and the specific traffic environment encountered.
Innovation Solution
A vehicle system that includes a processor and memory to select candidate routes, acquire traffic environment information, and simulate the reliability of autonomous driving using a trained model, outputting results to predict whether autonomous driving is executable on a given route, ensuring user understanding of potential driving risks and enabling informed route selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a trained model for autonomous driving is used, then autonomous driving can be executed, but the reliability changes according to traffic environment conditions
Solution Approach 1:
The system performs preliminary simulation of autonomous driving reliability for multiple candidate routes before the actual driving decision. By simulating the trained model's performance on each candidate route's traffic environment data in advance, the system determines which routes are suitable for autonomous driving, thereby ensuring reliability and predictability before execution.
2Stability of the object's composition
If the trained model parameters are fixed based on training conditions, then the model structure is stable, but the model cannot adapt to different traffic environments
Solution Approach 1:
The system evaluates the trained model's reliability separately for each candidate route based on its specific traffic environment characteristics. Instead of requiring the model to be universally adaptable to all environments, the system determines route-specific suitability by simulating the model's performance on each route's traffic data, allowing fixed parameters to maintain stability while achieving local adaptability.
3Extent of automation
If autonomous driving is attempted in all conditions, then automation coverage is maximized, but manual intervention is required when reliability is low
Solution Approach 1:
The system performs preliminary reliability simulation for multiple candidate routes before autonomous driving execution. By simulating the trained model's performance on each candidate route's traffic environment data in advance, the system identifies suitable routes and presents them to the user, thereby maximizing automation coverage while minimizing manual intervention through pre-evaluation.
Data Source
AI summary
The vehicle of the present disclosure includes at least one processor and at least one memory storing a plurality of instructions executed on the at least one processor. The plurality of instructions causes the at least one processor to select a candidate route to a destination and acquire information on a traffic environment of the selected candidate route. The plurality of instructions further causes the at least one processor to simulate, based on the information on the traffic environment, reliability in a case where the vehicle travels on the selected candidate route by the autonomous driving using a trained model for autonomous driving, and output a result of the simulation.