Personalized Lane Change Prediction Using Driver Cost Functions
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Solution Overview
Problem
Predicting lane change maneuvers of human-driven vehicles is challenging due to the inherent uncertainties in human driver behaviors, which complicates safe and efficient interactions between connected and automated vehicles (CAVs) and human-driven vehicles in mixed traffic environments.
Innovation Solution
A learning-based lane change prediction algorithm that evaluates driving behaviors of a target human driver, utilizing a hierarchical structure with an online decision prediction phase and a trajectories prediction phase, incorporating inverse reinforcement learning (IRL) to recover driver preferences, and employs a Long-Short Term Memory (LSTM) network trained on historical vehicle states for personalized lane change prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane change prediction methods are used, then the prediction process is simple, but the prediction accuracy is low due to inherent uncertainties in human driver behaviors
Solution Approach 1:
The prediction system is segmented into multiple independent modules: a decision prediction module that predicts lane change decisions, a trajectory prediction module that generates candidate trajectories, and a cost function module that evaluates trajectories. This segmentation allows each module to specialize in specific aspects of prediction, improving overall accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The system dynamically adapts to individual driver behaviors by learning personalized cost functions and preferences from historical data. The prediction model is not static but evolves to capture each driver's unique lane change patterns, making the system more accurate for human behavior prediction without requiring overly complex fixed rules
2Measurement precision
If personalized cost functions are recovered using inverse reinforcement learning, then the prediction becomes more accurate, but the computational complexity increases
Solution Approach 1:
The system performs preliminary learning of driver-specific cost functions during an offline training phase using inverse reinforcement learning. By pre-computing and storing personalized cost functions for each driver, the system avoids performing complex IRL calculations in real-time, thus improving online prediction accuracy while managing computational resources efficiently
Solution Approach 2:
Instead of performing complex IRL computations repeatedly, the system creates simplified copies or representations of driver preferences in the form of learned cost functions. These copied representations can be quickly applied during online prediction, reducing computational burden while maintaining prediction accuracy
3Manufacturing precision
If historical driving data is collected and processed through clustering algorithms, then the training data quality improves, but the data processing time increases
Solution Approach 1:
The system performs data clustering and labeling as a preliminary offline processing step before model training. By pre-processing historical driving data into labeled training sets with clustered features, the system improves data quality for training while avoiding time-consuming processing during real-time prediction operations
Data Source
AI summary
A learning-based lane change prediction algorithm, and systems and methods for implementing the algorithm, are disclosed. The prediction algorithm evaluates the driving behaviors of a target human driver and predicts lane change maneuvers based on those personalized driving behaviors. The algorithm may include an online lane change decision prediction phase and an offline prediction training and cost function recovery phase. During the offline training phase, a machine learning model may be trained based on historical vehicle states. During the online validation phase, driving data may be collected and fed to the trained model to predict a driver's lane change maneuver, identify potential vehicle trajectories, and determine a most probable vehicle trajectory based on a driver's cost function recovered during the offline phase.