Lane-Segment Behavior Prediction for Near-Future Road User Motion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to accurately predict the near-future behavior of road users, such as driving intentions and trajectories, which is crucial for autonomous driving systems (ADS) due to intrinsic uncertainties in human driving behaviors.
Innovation Solution
An assessment system that utilizes a neural network to process vehicle and road user data, encoding spatial and temporal information about lane segments and road users, to predict near-future behaviors by integrating data from digital maps, sensors, and historical information, employing multi-head and self-attention operations to model interactions between lane segments and road users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction methods are used for road user behavior, then the system complexity remains low, but the prediction accuracy and reliability are insufficient
Solution Approach 1:
The patent replaces traditional mechanical prediction methods with neural network-based computational systems. Multiple neural networks process spatial and temporal data from road users and lane segments to generate behavior predictions, substituting simple algorithmic approaches with advanced machine learning models that achieve higher accuracy despite increased complexity
Solution Approach 2:
The patent segments the prediction system into multiple specialized neural networks: one for processing road user states, another for lane segment characteristics, and a third for integrating spatial-temporal relationships. This segmentation allows each network to specialize in specific aspects of behavior prediction, improving overall accuracy while managing complexity through modular architecture
2Reliability
If simple prediction models are used, then the computational resources required are low, but the ability to capture spatial and temporal relationships is insufficient
Solution Approach 1:
The patent performs preliminary encoding of road user states and lane segment data into structured representations before feeding them to the prediction neural networks. This preprocessing step organizes spatial and temporal relationships in advance, allowing the main prediction networks to focus on pattern recognition rather than raw data processing, thereby improving reliability while optimizing energy consumption
Data Source
AI summary
An assessment system obtains a state of the vehicle, and identifies lane segments void from road users. Input having data associated with the lane segments, data associated with the road users states and historic data associated with the road users states and/or lane segments, is encoded into respective states-related data associated with the road users states and segment-related data associated with dynamic start and end boundaries of the lane segments. One or more neural networks encode the road users states in view of the lane segments spatially and temporally, and output spatial- and temporal-processed respective states-related data and segment-related data. The output data associated with one or more of the road users (target road users) is fed to at least a first behavior-predicting neural network to predict and output data indicating predicted near-future behavior of the target road users in view of one or more of the lane segments.