Temporal Recurrent Network for Online Action Detection in Autonomous Driving
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Solution Overview
Problem
Autonomous driving systems lack the ability to incorporate a driver's logic, attentive behavior, and causal reactions, leading to inefficiencies in situational understanding and adaptation in dynamic driving environments, particularly in time-sensitive traffic scenarios.
Innovation Solution
A computer-implemented method utilizing a temporal recurrent network for online action detection, which analyzes image data from a vehicle camera system to determine goal-oriented actions based on past, current, and predicted frames, and controls the vehicle to mimic naturalistic driving behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional autonomous driving systems use basic sensor data analysis, then the system complexity is low, but the situational understanding capability is insufficient
Solution Approach 1:
The system performs preliminary analysis of sensor data to identify potential situations and patterns before making driving decisions. By pre-processing and pre-classifying data, the system builds contextual understanding in advance, reducing information loss during critical decision-making moments without requiring overly complex real-time processing.
Solution Approach 2:
The patent introduces intermediate processing layers that act as mediators between raw sensor data and high-level driving decisions. These intermediate layers include contextual analysis modules and situational understanding components that bridge the gap between basic detection and complex decision-making, preserving information while managing system complexity.
2Loss of information
If the system analyzes elongated period sensor data, then the situational understanding improves, but the response time in time-sensitive scenarios deteriorates
Solution Approach 1:
The patent segments the analysis of sensor data into different temporal layers: short-term recent data for immediate responses, medium-term data for near-future planning, and long-term data for strategic situational understanding. This segmentation allows the system to process only relevant time windows for each decision type, reducing overall processing time while maintaining comprehensive understanding.
Solution Approach 2:
The system applies partial analysis to elongated time periods by focusing only on critical events and changes rather than processing all data points uniformly. For time-sensitive scenarios, the system performs excessive action by prioritizing recent data and skipping less critical historical analysis, ensuring rapid response without complete situational assessment.
3Measurement precision
If the system processes multiple image frames for action detection, then the action recognition accuracy improves, but the computational load increases
Solution Approach 1:
The patent extracts only the most informative features and key frames from the sequence of image data, removing redundant information before processing. By taking out and focusing on critical elements such as motion vectors, change detection results, and salient object features, the system maintains high action detection accuracy while significantly reducing the computational energy required for processing multiple frames.
Data Source
AI summary
A system and method for utilizing a temporal recurrent network for online action detection that include receiving image data that is based on at least one image captured by a vehicle camera system. The system and method also include analyzing the image data to determine a plurality of image frames and outputting at least one goal-oriented action as determined during a current image frame. The system and method further include controlling a vehicle to be autonomously driven based on a naturalistic driving behavior data set that includes the at least one goal-oriented action.