Autonomous Vehicle Erratic Driving Detection With Camera-Only Sensing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current self-driving vehicles require a substantial amount of hardware and sensors to function effectively, leading to increased costs and slower adoption due to the complexity and expense of these systems.
Innovation Solution
A vehicle system that utilizes a reduced set of optical sensors, combined with advanced software and machine learning algorithms, to achieve autonomous driving levels, particularly focusing on autonomous vehicle models generated by convolutional neural networks for image processing and vehicle control, allowing for lower hardware requirements while maintaining effective autonomous operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a substantial amount of hardware and sensors is used, then autonomous driving effectiveness is improved, but cost and system complexity increase
Solution Approach 1:
The patent extracts and removes unnecessary sensors from the autonomous driving system, retaining only the essential optical sensors (cameras) while eliminating redundant hardware such as LIDAR, radar, and other expensive sensor suites. This extraction approach maintains core autonomous driving functionality while significantly reducing system complexity and cost.
Solution Approach 2:
The patent replaces expensive, complex sensor hardware with more affordable optical sensors (cameras) that can be substituted or replaced more easily. This approach uses cheaper sensing components that leverage software intelligence rather than relying on costly specialized hardware, making the system more accessible and easier to maintain.
2Reliability
If a substantial amount of hardware and sensors is used, then autonomous driving effectiveness is improved, but adoption rate decreases due to cost and complexity
Solution Approach 1:
The patent employs affordable optical sensors instead of expensive specialized hardware, making autonomous vehicles more economically viable for mass production and widespread adoption. The use of standard camera technology rather than proprietary expensive sensors lowers the barrier to entry for manufacturers and consumers alike.
Solution Approach 2:
The patent substitutes mechanical and electronic sensor systems with optical-based camera systems processed through software and machine learning algorithms. This substitution reduces hardware complexity and manufacturing costs while maintaining or improving autonomous driving performance through intelligent software processing.
Data Source
AI summary
Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.