Probabilistic Agent Tracking Model for Autonomous Vehicles
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Solution Overview
Problem
Existing systems face challenges in accurately predicting the behaviors of dynamic agents in a surrounding environment, particularly when observations are partial or noisy, due to the complexity of the environment and occlusions, which hinders autonomous navigation and control of vehicles.
Innovation Solution
A prediction system that employs a probabilistic model combining a transition model and an agent model to estimate future states of road agents using present and previous observations, accounting for dynamic behaviors and actions, and controls vehicle systems accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensors acquire electronic information continuously to track dynamic agents, then the completeness of environmental observation improves, but the complexity of processing and estimating states increases
Solution Approach 1:
The system segments the complex state estimation problem into discrete observable states and transitions between them. By defining a finite state space for agent behaviors (e.g., moving, stationary, turning) and using sensor data to determine current state and transitions, the system breaks down the continuous complex environment into manageable discrete components that can be processed independently.
Solution Approach 2:
The patent introduces an intermediary processing layer that mediates between raw sensor data and high-level state estimation. This intermediary system uses electronic information from sensors to infer agent states and behaviors, acting as a bridge that simplifies the complexity by processing intermediate representations rather than directly handling all raw sensor data and environmental variables simultaneously.
2Measurement precision
If the system accounts for latent aspects and previous observations to improve prediction accuracy, then the precision of behavior prediction improves, but the computational requirements and system complexity increase
Solution Approach 1:
The system performs preliminary actions by continuously acquiring and storing electronic information about agents in previous observations. This pre-collected data includes agent positions, behaviors, and environmental context, which are prepared in advance and organized for efficient retrieval. When prediction is needed, this pre-processed information is already available, reducing the computational burden during the actual prediction moment while maintaining high precision.
Solution Approach 2:
The system implements feedback mechanisms where previous observations and state estimations are fed back into the prediction process. The electronic information from past observations serves as feedback that refines current state estimation and improves future predictions. This feedback loop allows the system to learn from historical data and continuously improve prediction accuracy without requiring complete re-analysis of all past data each time.
3Reliability
If the system uses robust sensor configuration to reduce occlusions, then the reliability of agent tracking improves, but the device complexity and cost increase
Solution Approach 1:
The patent merges multiple sensor types and data sources into a unified tracking system. By combining electronic information from different sensors (cameras, LIDAR, radar) into a single integrated system that processes data together, the achieves improved reliability through sensor fusion. This merging approach allows the system to compensate for occlusions by using complementary information from multiple sensors, reducing the need for overly complex individual sensor configurations while maintaining high tracking reliability.
Data Source
AI summary
System, methods, and other embodiments described herein relate to modeling dynamic agents in a surrounding environment of an ego vehicle. In one embodiment, a method includes, in response to receiving sensor data including present observations of a road agent of the dynamic agents in the surrounding environment, identifying previous observations of the road agent from an electronic data store. The method includes estimating a future state of the road agent using at least the present observations and the previous observations of the road agent to compute the future state according to a probabilistic model comprised of a transition model that accounts for dynamic behaviors of the road agent to characterize transitions between states, and an agent model that accounts for actions of the road agent. The method includes controlling one or more vehicle systems of the ego vehicle according to the future state of the road agent.