Vehicle Mode Selection Using Predictive Environmental Perception
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Solution Overview
Problem
Drivers often face confusion and errors when manually selecting the appropriate vehicle mode for varying environments, leading to suboptimal performance and comfort.
Innovation Solution
An automated mode selection system using real-time perception data and a fitted inference model to predict and select the most suitable vehicle subsystem modes based on the upcoming environment, reducing user error and improving performance and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated mode selection is implemented, then mode selection accuracy is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary analysis of upcoming environment using perception data before the vehicle actually encounters the condition. The inference model predicts future mode requirements based on anticipated terrain, weather, or traffic conditions, allowing the system to proactively select modes before they are strictly needed, improving accuracy while managing complexity through advance preparation
Solution Approach 2:
The patent introduces an inference model as an intermediary component that bridges perception data and mode selection. This model processes environmental information and translates it into predicted mode requirements, acting as a mediator that simplifies the overall system architecture while improving decision accuracy. The intermediary handles the complexity internally, presenting a cleaner interface to the rest of the vehicle system
2Loss of time
If reactive mode selection is used, then system simplicity is maintained, but response time to environmental changes is delayed
Solution Approach 1:
The system analyzes perception data to predict upcoming environmental conditions before the vehicle reaches them. By anticipating future states such as approaching terrain changes or weather conditions, the system can prepare and switch modes in advance, significantly reducing response delay compared to waiting for conditions to actually occur
Solution Approach 2:
The patent implements dynamic mode selection that adapts to changing environmental conditions in real-time. The inference model continuously processes incoming perception data and adjusts mode predictions dynamically, allowing the system to respond flexibly to varying conditions while maintaining proactive timing. This dynamic approach balances automation complexity with improved response characteristics
3Adaptability or versatility
If multiple vehicle modes are provided, then adaptability to different environments is improved, but ease of operation deteriorates due to manual selection complexity
Solution Approach 1:
The system enables the vehicle to automatically select appropriate modes based on environmental perception data and inference model predictions. The vehicle essentially serves itself by autonomously determining the optimal mode for current and upcoming conditions, eliminating the need for driver intervention. This self-service approach maintains high environmental adaptability while completely removing the operational burden from the driver
Solution Approach 2:
The automated system performs preliminary assessment of environmental conditions and predicts future mode requirements without waiting for driver input. By proactively analyzing perception data and making mode selections in advance, the system ensures the vehicle is always in the optimal mode for upcoming conditions, maintaining versatility while improving ease of operation through complete automation
Data Source
AI summary
Method and system for automatically selecting a mode for a vehicle, including: receiving, through a perception system of the vehicle, real-time perception data representing an environment in a direction of travel of the vehicle; receiving, through a vehicle sensor system; and predicting, using a first fitted inference model, based on the real-time perception data, first-subsystem candidate mode predictions for a first-subsystem of the vehicle. The first-subsystem candidate mode predictions correspond to a set of predefined modes for the first-subsystem, each of the predefined modes defining a respective set of one or more operating parameters for the first-subsystem. A first-subsystem mode is determined based on the first-subsystem candidate mode predictions.