Machine-learned model training for pedestrian attribute and gesture detection
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Solution Overview
Problem
Autonomous vehicles face challenges in safely navigating environments with pedestrians due to the inability of existing machine-learned models to accurately detect contextual information and pedestrian intentions, leading to potential unsafe decisions.
Innovation Solution
The development of techniques for detecting pedestrian attributes and gestures using machine-learned models, which process sensor data from vehicles to determine classifications, activities, poses, and gestures, enabling informed decision-making for safe vehicle operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine-learned models are used for object detection, then basic object identification is achieved, but contextual information and pedestrian intentions cannot be accurately detected
Solution Approach 1:
The detection system is segmented into multiple specialized machine-learned models: one for detecting pedestrian attributes (clothing color, accessories) and another for detecting gestures (hand signals, body language). This segmentation allows each model to specialize in specific types of contextual information, improving overall detection accuracy while capturing comprehensive contextual data that a single general-purpose model would miss.
Solution Approach 2:
The system transitions from detecting only basic object presence to detecting multiple dimensions of information simultaneously: physical attributes (clothing color, accessories), dynamic gestures (hand signals, body orientation), and inferred intentions. This multi-dimensional approach transforms the detection space from simple object classification to comprehensive contextual understanding, enabling accurate prediction of pedestrian intentions.
2Reliability
If basic object detection is performed, then processing speed is maintained, but unsafe decisions may occur due to inability to detect pedestrian intentions
Solution Approach 1:
The complex detection task is divided into separate machine-learned models for different functions: attribute detection, gesture detection, and intention prediction. Each model is optimized for its specific function, which improves reliability by reducing errors that would occur in a monolithic system while managing complexity through modular architecture that can be processed independently.
Solution Approach 2:
The system introduces an intermediary processing layer that takes raw detection data from multiple models and synthesizes it into predicted pedestrian intentions. This intermediary layer acts as a mediator between basic detection outputs and vehicle control decisions, transforming complex multi-source data into actionable safety-critical information without requiring the entire system to handle all complexity simultaneously.
3Measurement precision
If multiple attributes and gestures are detected, then pedestrian intentions can be accurately predicted, but data processing requirements increase
Solution Approach 1:
Processing is segmented into parallel specialized models for attribute detection and gesture detection, each optimized for its specific data type. This allows efficient processing of different data streams independently, reducing the computational energy required compared to a single model processing all data types, while still achieving accurate intention prediction through integration of both detection streams.
Data Source
AI summary
Techniques for detecting attributes and/or gestures associated with pedestrians in an environment are described herein. The techniques may include receiving sensor data associated with a pedestrian in an environment of a vehicle and inputting the sensor data into a machine-learned model that is configured to determine a gesture and/or an attribute of the pedestrian. Based on the input data, an output may be received from the machine-learned model that indicates the gesture and/or the attribute of the pedestrian and the vehicle may be controlled based at least in part on the gesture and/or the attribute of the pedestrian. The techniques may also include training the machine-learned model to detect the attribute and/or the gesture of the pedestrian.