Vehicle Light State Analysis for Low-Latency Turn and Brake Prediction
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Solution Overview
Problem
Conventional methods for predicting vehicle turning and braking actions in autonomous vehicles are hindered by high latency and the need for extensive labeled data, and they often rely on passive/reactive approaches that infer actions only after significant vehicle motion or slowing, lacking clarity on feature usage.
Innovation Solution
A three-stage approach using deep learning techniques to detect and analyze vehicle light patterns, predicting actions by comparing images over time to determine light status changes, allowing for early and accurate anticipation of vehicle maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural network-based approaches use images of entire vehicles as inputs to predict turn and brake actions, then the model can directly predict vehicle actions, but the approach suffers from high latency and requires a huge amount of ground truth labeled data
Solution Approach 1:
The patent segments the vehicle image into multiple regions of interest (ROIs) including front view, rear view, left side, right side, and top view. Each ROI is processed separately to extract specific features such as light patterns, tire orientations, and vehicle orientations. This segmentation reduces the computational complexity of processing entire vehicle images while maintaining prediction accuracy by focusing on critical action-indicative regions.
Solution Approach 2:
The patent extracts specific features from the segmented ROIs, particularly focusing on light patterns (turn signals, brake lights), tire orientations, and vehicle orientations. By extracting only the relevant features needed for prediction rather than processing entire vehicle images, the system reduces computational latency and data requirements while maintaining the ability to accurately predict turn and brake actions.
2Measurement precision
If conventional neural network-based approaches use entire vehicle images for prediction, then the model can capture all vehicle features, but it becomes a black box with no clarity on which features are used for prediction
Solution Approach 1:
The patent segments the vehicle image into distinct ROIs (front, rear, left, right, top views) and processes each to extract specific features. This segmentation provides clarity on which regions contribute to which predictions, making the model's decision-making process more transparent and interpretable while maintaining comprehensive feature capture.
Solution Approach 2:
The patent applies different processing and feature extraction methods to different ROIs based on their specific characteristics. For example, light patterns are extracted from specific regions, tire orientations from others, and vehicle orientations from yet others. This localized processing enhances interpretability by clearly associating specific features with specific prediction decisions.
3Reliability
If passive/reactive approaches are used to infer vehicle actions only after significant motion or slowing, then the system can detect actions with sufficient evidence, but it cannot predict actions before they occur
Solution Approach 1:
The patent performs preliminary analysis of light patterns, tire orientations, and vehicle orientations to predict turn and brake actions before they occur. By monitoring these features continuously and analyzing their changes, the system can anticipate driver intentions and predict upcoming actions, providing early warning rather than waiting for actions to fully manifest.
Solution Approach 2:
The patent uses multiple ROIs and feature types (lights, tires, orientations) to gather sufficient evidence for prediction. By collecting and analyzing partial information from various sources simultaneously, the system achieves reliable predictions with sufficient evidence while maintaining the ability to predict actions before they fully occur.
Data Source
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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 detects, 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 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.