Autonomous Vehicle Actor Motion Forecasting for Surprise Movement Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in detecting and reacting to unexpected movements of actors, such as pedestrians or vehicles, which can lead to potential collisions due to limitations in predicting unforeseen behaviors and reacting in a timely manner.
Innovation Solution
A computer-implemented method and system that uses motion forecast data to identify 'failsafe regions' where the likelihood of an actor following an unexpected path or entering an unexpected area is below a threshold, allowing for proactive adjustments in the vehicle's movement plan, including prioritizing sensor data processing and potential preliminary actions like braking, to enhance reaction time and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses traditional motion prediction methods, then the system complexity is low, but the ability to detect unexpected actor movements is insufficient
Solution Approach 1:
The system segments the monitoring space into multiple regions of interest (ROIs) based on predicted actor trajectories. Each ROI is monitored independently with dedicated sensor resources, allowing the system to focus computational power on critical areas while maintaining overall system manageability and improving detection accuracy for unexpected movements.
Solution Approach 2:
The system performs preliminary motion forecasting to predict future actor positions and pre-identifies regions where unexpected movements are most likely to occur. By preparing ROI definitions and sensor allocation in advance based on predicted trajectories, the system improves detection reliability without requiring complex real-time decision-making for every sensor allocation decision.
2Loss of time
If the autonomous vehicle processes all sensor data in real-time, then the detection coverage is comprehensive, but the reaction time to unexpected movements is delayed
Solution Approach 1:
The system applies different processing qualities to different spatial regions. High-priority ROIs where unexpected movements are most likely receive intensive real-time sensor processing and rapid analysis, while lower-priority areas receive standard monitoring. This localized quality differentiation reduces overall processing time for critical events while maintaining adequate detection coverage across the entire environment.
Solution Approach 2:
The system pre-identifies regions of interest based on motion forecasting before unexpected movements occur. By having ROIs and sensor allocation strategies prepared in advance, the system can immediately focus processing resources on critical areas when movements deviate from predictions, significantly reducing reaction time without sacrificing comprehensive detection coverage.
3Reliability
If the autonomous vehicle monitors the entire surrounding environment uniformly, then the detection coverage is complete, but the computational resources are insufficient
Solution Approach 1:
The system segments the monitoring environment into multiple regions of interest based on predicted actor trajectories and potential unexpected movement zones. By dividing the computational task into region-specific processing, the system can allocate computational resources efficiently to high-priority areas while maintaining detection reliability where it matters most, rather than uniformly processing the entire surrounding environment.
Solution Approach 2:
The system applies enhanced processing quality and resource allocation to specific local regions (ROIs) where unexpected movements are most likely to occur, based on motion forecasting. Other areas receive standard monitoring with reduced computational overhead. This local quality differentiation maintains detection reliability in critical zones while conserving overall computational resources.
Data Source
AI summary
Systems and methods for detecting a surprise or unexpected movement of an actor with respect to an autonomous vehicle are provided. An example computer-implemented method can include, for a first compute cycle, obtaining motion forecast data based on first sensor data collected with respect to an actor relative to an autonomous vehicle; and determining, based on the motion forecast data, failsafe region data representing an unexpected path or area where a likelihood of the actor following the unexpected path or entering the unexpected area is below a threshold. For a second compute cycle after the first compute cycle, the method can include obtaining second sensor data; determining, based on the second sensor data and the failsafe region data, that the actor has followed the unexpected path or entered the unexpected area; and in response to such determination, determining a deviation for controlling a movement of the autonomous vehicle.