Reference Model Selection for Driving Trajectory Optimization
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Solution Overview
Problem
Drivers face challenges in determining optimal vehicle trajectories during driving, especially when considering various factors such as disturbances, which can complicate the driving process.
Innovation Solution
An information processing apparatus and method that acquires parameters from sensors, extracts and clusters driving models, and selects or generates a reference model based on these parameters to provide an expected driving value, improving ease of driving by predicting and displaying optimal behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a driver follows conventional driving without reference models, then the driving process is simple, but the driver cannot obtain expected values considering various factors such as disturbances
Solution Approach 1:
The patent introduces a reference model as an intermediary between the driving situation and the driver's decision-making. The reference model processes sensor data, considers various factors including disturbances, and provides expected values that guide driving decisions, thereby improving driving optimality without requiring the driver to directly analyze complex situations
Solution Approach 2:
The system performs self-service by automatically extracting features from sensor data, clustering them into reference models, and selecting appropriate models based on current driving conditions. This automation eliminates the need for manual analysis of driving situations while providing optimized driving guidance
2Adaptability or versatility
If the system extracts and clusters multiple driving models from sensor parameters, then the system can provide situation-dependent reference models, but the model extraction and selection process becomes more complex
Solution Approach 1:
The patent segments the driving situation space into distinct clusters by extracting features from sensor data and grouping similar situations together. Each cluster corresponds to a reference model with specific driving characteristics, allowing the system to adapt to different situations by selecting the appropriate pre-extracted model rather than processing each situation uniquely
Solution Approach 2:
The system performs preliminary action by pre-extracting and pre-clustering driving models during an offline phase. This allows the online system to simply select from pre-computed models based on current conditions, avoiding the computational complexity of extracting and clustering models in real-time while maintaining high situation adaptability
Data Source
AI summary
According to the present disclosure, there is provided an information processing apparatus including: a parameter acquisition unit (102) that acquires parameters collected from a sensor; and a reference model acquisition unit (108) that generates or selects a reference model recommended depending on a situation on the basis of a model extracted on the basis of the parameters. With this configuration, the driver can obtain an expected value in consideration of various factors during driving, disturbance, or the like.