Proximate Vehicle Intention Prediction for Collision-Aware Motion Planning
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Solution Overview
Problem
Conventional autonomous vehicle control systems fail to accurately predict collisions with dynamic obstacles, particularly other vehicles, due to the lack of consideration for the interaction between the autonomous vehicle and proximate dynamic vehicles, leading to suboptimal behavior and potential collisions.
Innovation Solution
A system and method for proximate vehicle intention prediction, which utilizes sensors and machine learning techniques to collect perception data, predict the behavior and intentions of surrounding vehicles, and integrate this information into motion planning to enhance the autonomous vehicle's ability to navigate safely and efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional autonomous vehicle control systems use polynomial expressions or mathematical functions to represent spatial information for avoiding stationary obstacles, then the control operations can be determined, but the system cannot accurately predict collisions with dynamic obstacles due to lack of consideration for interaction between vehicles
Solution Approach 1:
The system performs preliminary action by predicting the future intentions and actions of proximate dynamic vehicles before actual collisions occur. The intention prediction module analyzes current states and trajectories of surrounding vehicles to forecast their future behaviors, enabling the autonomous vehicle to proactively adjust its path to avoid potential collisions with dynamic obstacles.
Solution Approach 2:
The patent introduces an intermediary intention prediction module that mediates between the conventional control system and the dynamic environment. This module acts as a bridge by interpreting the intentions of other vehicles and translating them into predictive information that the motion planning system can use, without requiring complete redesign of the underlying control architecture.
2Reliability
If the autonomous vehicle follows a computed driving path without considering proximate dynamic vehicles, then the vehicle can maintain simple control operations, but the unexpected behavior of proximate dynamic obstacles may result in collisions
Solution Approach 1:
The control system is segmented into distinct functional modules: the conventional motion planning module for basic path following, and the new intention prediction module for dynamic obstacle analysis. This segmentation allows the system to maintain simple control operations for normal driving while adding specialized functionality only where needed for predicting and responding to dynamic obstacles.
Solution Approach 2:
The system performs preliminary analysis of proximate dynamic vehicles' intentions before they become actual threats. By continuously predicting the future states of surrounding vehicles, the system prepares advance collision avoidance strategies, enabling safe navigation without requiring complex real-time reactions to unexpected behaviors.
3Productivity
If the system integrates intention prediction for proximate dynamic vehicles, then the optimal behavior and collision avoidance improve, but the computational requirements and system complexity increase
Solution Approach 1:
The intention prediction module is designed with multi-functionality to handle various types of dynamic obstacles (vehicles, pedestrians, cyclists) using a unified prediction framework. This universal approach improves motion planning efficiency by providing comprehensive collision avoidance capabilities without requiring separate specialized systems for each obstacle type, thereby limiting the increase in overall system complexity.
Data Source
AI summary
A system and method for proximate vehicle intention prediction for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; extract features from the perception data to detect a proximate vehicle in the vicinity of the host vehicle; generate a trajectory of the detected proximate vehicle based on the perception data; use a trained intention prediction model to generate a predicted intention of the detected proximate vehicle based on the perception data and the trajectory of the detected proximate vehicle; use the predicted intention of the detected proximate vehicle to generate a predicted trajectory of the detected proximate vehicle; and output the predicted intention and predicted trajectory for the detected proximate vehicle to another subsystem.