Vehicle Light State Detection for Early Turn and Brake Prediction
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Solution Overview
Problem
Conventional methods for predicting vehicle turn and brake actions are inefficient due to their passive/reactive nature and require extensive labeled data, with neural network-based approaches suffering from high latency and lack of transparency in feature usage.
Innovation Solution
A three-stage approach using deep learning techniques to detect and analyze vehicle light patterns, predicting actions by comparing changes in turn signal and brake light states over time, allowing for early and accurate response by autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional neural network-based approaches are used to predict vehicle turn and brake actions, then prediction capability is achieved, but latency increases and transparency of feature usage is lost
Solution Approach 1:
The patent segments the vehicle prediction problem into distinct components: detecting vehicle lights (turn signals, brake lights), determining light state changes, and predicting actions based on these changes. This segmentation allows for more efficient processing compared to using entire vehicle images in conventional neural networks, reducing latency while maintaining prediction capability.
Solution Approach 2:
The patent extracts specific relevant features (vehicle light patterns and state changes) from the complex visual data, rather than processing entire vehicle images. By taking out only the critical information (light on/off states and their changes), the system achieves prediction capability with reduced computational latency and improved transparency.
2Reliability
If conventional neural network-based approaches are used to predict vehicle actions, then prediction is achieved, but the model becomes a black box with no transparency in feature usage
Solution Approach 1:
The patent introduces an intermediary layer between image input and action prediction: the detection and tracking of vehicle light states. This intermediary provides transparent, interpretable intermediate representations (light on/off states and their changes over time) that bridge the gap between raw visual data and predicted actions, maintaining both prediction capability and transparency.
Solution Approach 2:
The patent utilizes changes in light states (analogous to color changes) as explicit, interpretable indicators of vehicle intent. By monitoring transitions in light states (e.g., turn signal from off to on, brake light intensity changes), the system provides transparent feature usage that maintains prediction capability while avoiding the black box problem.
3Ease of manufacture
If passive/reactive approaches are used to observe vehicle motion, then implementation is simple, but prediction capability is limited and only detects actions after they have started
Solution Approach 1:
The patent implements preliminary action by detecting and monitoring vehicle light states before actual turn or brake actions occur. Since vehicles typically activate turn signals and brake lights before executing maneuvers, this approach enables early prediction of vehicle actions, improving reliability while maintaining implementation feasibility through straightforward light detection and state change tracking.
Data Source
AI summary
This application is directed to predicting vehicle turn and brake actions for at least partially autonomous vehicle driving. A first vehicle obtains a plurality of images along a road. For each image of the plurality of images, the first vehicle extracts, from the image, a plurality of image regions each corresponding to a respective vehicle light of a second vehicle positioned on the road near the first vehicle. The first vehicle generates, for each image, a composite image that includes the plurality of image regions. The first vehicle determines, for each image region, a probability that a respective vehicle light of the second vehicle changed its state. The first vehicle predicts a vehicle action of the second vehicle based on the probability for each image region. The first vehicle at least partially autonomously driving the first vehicle based on the predicted vehicle action of the second vehicle.