Lane Change Intention Prediction Network for Autonomous Route Updates
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Solution Overview
Problem
Existing vehicle lane change intention prediction methods are inaccurate and rigid, failing to consider sufficient surrounding vehicle information, leading to suboptimal autonomous driving decisions and increased traffic accident risk.
Innovation Solution
A lane change intention prediction network with multiple sub-networks processes vehicle state, interaction, and road network information in parallel, performing feature extraction and fusion to accurately predict lane change intentions in various scenarios without excessive preset rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane change prediction methods are used, then the system is simple to implement, but the prediction accuracy is low and cannot consider sufficient surrounding vehicle information
Solution Approach 1:
The prediction network is divided into multiple independent sub-networks, each responsible for processing specific types of information (vehicle state, interaction, road network). This segmentation allows each sub-network to specialize in extracting features from particular data sources, improving overall prediction accuracy while maintaining manageable complexity through modular design
Solution Approach 2:
The multi-sub-network architecture serves multiple functions simultaneously: it processes diverse information types (vehicle dynamics, spatial relationships, road context), handles different feature extraction tasks, and integrates various data sources. This multi-functionality enables comprehensive consideration of surrounding vehicle information without requiring separate systems for each task
2Measurement precision
If multiple types of vehicle information are processed in parallel, then the prediction accuracy is improved, but the computational complexity increases
Solution Approach 1:
By segmenting the computational workload across multiple specialized sub-networks processing different information types in parallel, the system can optimize resource allocation for each sub-network. This reduces redundant computations and enables more efficient use of computational power compared to a single monolithic network processing all information sequentially or in a unified manner
3Adaptability or versatility
If excessive preset rules are used, then the system can handle various scenarios, but the system becomes rigid and loses adaptability
Solution Approach 1:
The system replaces traditional mechanical rule-based decision-making with an intelligent neural network model. Instead of relying on excessive preset rules that make the system rigid, the neural network learns adaptive patterns from data, enabling the system to handle various scenarios with flexibility and adaptability while maintaining stability through the learned representations
Data Source
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AI summary
A vehicle automatic control method, a lane change intention prediction network training method, a vehicle automatic control apparatus, and a lane change intention prediction network training apparatus, and an electronic device. The method comprises: receiving multiple types of vehicle traveling information of a target vehicle (702, 703, 704) (S800); inputting the multiple types of vehicle traveling information of the target vehicle (702, 703, 704) into a lane change intention prediction network, wherein the lane change intention prediction network comprises multiple subnetworks in one-to-one correspondence with the multiple types, and the lane change intention prediction network is used for predicting a lane change intention of a vehicle in a traveling state (S810); respectively performing feature extraction on each vehicle traveling information by means of each subnetwork, and outputting a feature extraction result (S820); performing feature fusion on the feature extraction results output by the subnetworks, and predicting a lane change intention of the target vehicle (702, 703, 704) according to a feature fusion result (S830); and updating an autonomous driving route of the current vehicle (701) according to the lane change intention of the target vehicle (702, 703, 704) (S840).