Visual Language Models for Autonomous Vehicle Event Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently detecting and responding to various objects and events in their environment, such as animals, fires, road construction, and dust clouds, which can affect their operation and require timely control measures.
Innovation Solution
The implementation of a visual language model that processes sensor data, including RGB images, LiDAR point clouds, and radar images, using a neural network trained on image-text pairs to identify objects or events of interest, enabling the autonomous vehicle to determine the presence of relevant objects or events and adjust its operation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor processing methods are used to detect objects and events in the environment, then the system can identify basic obstacles, but the detection accuracy and response time for diverse objects (animals, fires, road construction, dust clouds) are insufficient
Solution Approach 1:
The patent introduces a visual language model as an intermediary component between the sensor system and the control system. This model processes sensor data and generates natural language descriptions of detected objects and events, enabling more accurate and diverse object recognition including animals, fires, road construction, and dust clouds, while maintaining efficient real-time processing for autonomous vehicle operation.
2Adaptability or versatility
If a visual language model trained on image-text pairs is implemented to process sensor data, then the ability to identify and classify diverse objects and events is significantly improved, but the computational complexity and processing requirements increase
Solution Approach 1:
The visual language model serves multiple functions simultaneously: it processes various types of sensor data (RGB images, LiDAR point clouds, radar images), performs object detection, classifies diverse objects and events, and generates natural language descriptions. This multi-functionality reduces the need for separate specialized systems for each detection task, thereby managing complexity while enhancing versatility.
3Reliability
If multiple sensor types (RGB cameras, LiDAR, radar) are integrated to capture comprehensive environmental data, then the detection coverage and information quality improve, but the data processing load and system complexity increase
Solution Approach 1:
The patent merges multiple sensor data types (RGB images, LiDAR point clouds, radar images) into a unified processing pipeline through the visual language model. By combining these diverse data sources and processing them through a single integrated model, the system achieves reliable detection of diverse objects and events while optimizing energy consumption compared to running separate processing systems for each sensor type.
Data Source
AI summary
A method is provided, that includes: receiving camera data from a perception system of an autonomous vehicle; and providing the camera data to a visual language model, where the visual language model includes a mapping of a corpus of images and a corpus of text to a common parameter space. The method further includes: receiving from the visual language model an output corresponding to one or more text tokens; accessing a configuration file comprising a plurality of text tokens representing a plurality of objects or events of interest to the autonomous vehicle; and identifying a respective object or event of interest in an environment of the autonomous vehicle by determining that a text token of the output matches a respective one of the plurality of text tokens in the configuration file. The autonomous vehicle can then be controlled based at least in part on the respective object or event of interest.