Vehicle Future Path Estimation in Dynamic Road Scenes
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Solution Overview
Problem
Existing advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems face limitations in handling the dynamic and varied nature of road environments, particularly in detecting and predicting future paths due to the infinitesimal variety and detail of objects, shadows, and moving vehicles.
Innovation Solution
A system utilizing multiple cameras and processors to process images, employing deep learning algorithms to estimate a vehicle's future path, incorporating piece-wise affine functions, convolutions, and rectifier linear units, and integrating with vehicle controls for steering and alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If preconfigured object classifiers are used to detect objects in the environment, then the system can identify predefined objects, but the system fails to handle the infinitesimal variety and detail of road environments including dynamic objects, shadows, and moving vehicles
Solution Approach 1:
The patent replaces traditional mechanical object classification systems with a deep learning-based neural network system. The neural network processes images directly to estimate future vehicle paths, substituting the rigid preconfigured classifier approach with a flexible, data-driven model that can adapt to the infinitesimal variety of road environments, dynamic objects, shadows, and moving vehicles without requiring explicit programming for each scenario
Solution Approach 2:
The system changes the fundamental parameter of object detection from categorical classification to continuous path estimation. Instead of assigning discrete object labels, the neural network predicts continuous future path coordinates, allowing the system to handle any object type or environmental condition by estimating its impact on the vehicle's future trajectory rather than requiring preconfigured classifiers for each object category
2Measurement precision
If traditional image processing methods are used, then the system can process images efficiently, but the system cannot accurately estimate future paths in complex environments with dynamic objects and poor lane markings
Solution Approach 1:
The patent substitutes traditional image processing algorithms with a deep learning neural network. The neural network automatically learns complex feature representations from images and directly outputs future path estimates, replacing multi-step traditional processing pipelines with a unified end-to-end model that achieves superior accuracy in complex environments while managing computational complexity through efficient network architecture design
3Reliability
If the system uses deep learning algorithms to estimate future paths, then the system can accurately predict paths in complex environments, but the system requires significant computational resources and training data
Solution Approach 1:
The patent applies preliminary action by training the neural network offline using large datasets before deployment. The extensive computational work and data processing are performed in advance during the training phase, creating a pre-trained model that can then make accurate predictions with minimal computational resources during actual vehicle operation, thus resolving the contradiction between prediction reliability and real-time computational complexity
Data Source
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AI summary
A system and method estimate a future path ahead of a current location of a vehicle. The system includes at least one processor programmed to: obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road; obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads; apply the trained system to the image of the environment ahead of the current arbitrary location of the vehicle; and provide, based on the application of the trained system to the image, an estimated future path of the vehicle ahead of the current arbitrary location.