Stationary Agent Classification for Autonomous Vehicle Intent Prediction
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately determining the intent of stationary agents in their environment, which can lead to collisions and inefficient navigation, as existing systems struggle to differentiate between active and non-active participants in traffic flow.
Innovation Solution
A system utilizing a classification neural network that processes vectorized representations of sensor data to predict the stationary state of agents, such as parked, double parked, pulled over, or stalled, enabling the vehicle to plan safe and smooth trajectories by distinguishing between active and non-active traffic participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems are used to detect stationary agents, then the system can identify objects in the environment, but it cannot accurately differentiate between active and non-active traffic participants
Solution Approach 1:
The patent segments the classification task into multiple discrete stationary states (parked, double parked, pulled over, stalled, active participant) rather than treating all stationary agents uniformly. This segmentation enables the neural network to differentiate between various types of stationary agents with distinct intents, thereby improving measurement precision of agent intent while preserving information about specific agent states.
2Measurement precision
If the system processes detailed sensor data for all agents, then prediction accuracy improves, but computational resources increase
Solution Approach 1:
The patent employs a computationally efficient neural network architecture that processes sensor data in a streamlined manner. By using a relatively simple classification network rather than complex deep learning models, the system achieves acceptable prediction accuracy with lower computational resource consumption, effectively using a 'cheaper' processing approach that is sufficient for the task.
3Reliability
If the system classifies all stationary agents in detail, then navigation safety improves, but the complexity of the detection system increases
Solution Approach 1:
The classification system is segmented into distinct categories of stationary states, allowing the autonomous vehicle to apply different navigation strategies for each category. This segmentation improves reliability by enabling more accurate collision avoidance decisions while managing system complexity through a structured classification approach rather than requiring overly complex continuous analysis.
Solution Approach 2:
The system dynamically adjusts its classification and response based on the detected stationary state. By implementing a multi-class classification system that can adaptively categorize agents into different states (parked, stalled, pulled over, etc.), the system achieves high reliability in collision avoidance while maintaining manageable complexity through dynamic state-based decision making.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting the intent of an agent in an environment. One of the methods includes obtaining context data characterizing an environment, the context data comprising data characterizing a plurality of agents in the environment; and generating, based on processing the context data using a neural network having a plurality of network parameters, a respective predicted stationary state classification over a plurality of categories for each of one or more target agents of the plurality of agents in the environment, the categories including one or more categories that indicate that the target agent is a stationary non-participant of traffic in the environment.