Autonomous Vehicle Intent Prediction for Road User Safety
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Solution Overview
Problem
Autonomous vehicles face challenges in predicting the intent of road users, such as pedestrians and bicyclists, as existing systems primarily focus on predicting their behavior for a short period rather than understanding their desired actions, leading to potential safety issues and inefficient navigation.
Innovation Solution
The implementation of a method and system that uses a perception system to identify road users, generate predictions of their intent and behavior, and adjust vehicle maneuvers to accommodate their desired actions, incorporating behavior-time models and intent models to anticipate and facilitate the actions of road users, such as crossing the road or changing lanes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle waits for the road user to complete their action before maneuvering, then safety is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by maneuvering the vehicle in advance based on predicted intent before the road user actually completes their action. The intent prediction model forecasts future actions (crossing, turning, merging) and the vehicle proactively adjusts its trajectory now, rather than waiting for the action to manifest. This resolves the contradiction by enabling early intervention that maintains safety while minimizing time loss.
2Measurement precision
If the system only predicts behavior for short period, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system transitions from predicting only immediate behavior (one dimension of time) to predicting intent across multiple time horizons. The intent prediction model adds a new dimension by forecasting future actions beyond the immediate short term, capturing what road users plan to do. This resolves the contradiction by expanding the temporal dimension of prediction while maintaining precision through the specialized intent model.
3Productivity
If the vehicle maneuvers early based on intent prediction, then productivity is improved, but reliability worsens
Solution Approach 1:
The vehicle performs preliminary maneuvers based on predicted intent while continuously monitoring for action confirmation. The system proactively adjusts trajectory in advance of the road user's actual action, improving navigation efficiency. Simultaneously, it maintains safety by verifying predictions and being ready to adjust if the predicted intent doesn't materialize, resolving the contradiction between early action and safety.
Solution Approach 2:
The system implements feedback mechanisms where predicted intent is continuously monitored against actual road user actions. If the predicted intent is confirmed, the preliminary maneuver proceeds; if not, the vehicle adjusts its trajectory. This feedback loop resolves the contradiction by enabling early productivity-generating actions while maintaining safety through continuous verification and adjustment.
Data Source
AI summary
As an example, data identifying characteristics of a road user as well as contextual information about the vehicle's environment is received from the vehicle's perception system. A prediction of the intent of the object including an action of a predetermined list of actions to be initiated by the road user and a point in time for initiation of the action is generated using the data. A prediction of the behavior of the road user for a predetermined period of time into the future indicating that the road user is not going to initiate the action during the predetermined period of time is generated using the data. When the prediction of the behavior indicates that the road user is not going to initiate the action during the predetermined period of time, the vehicle is maneuvered according to the prediction of the intent prior to the vehicle passing the object.