V2X-Driven Perception Fields for Robust Autonomous Driving
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous vehicle technologies face scalability issues due to limited field of view, lighting and weather challenges, and occlusions, leading to detection errors and noisy localization/kinematics, which are costly to address with expensive sensors and infrastructure, and require vast data and computational resources for machine learning-based solutions that struggle with edge cases.
Innovation Solution
The concept of perception fields, where road objects are represented as virtual force fields learned through ADAS and AV software, allowing for a holistic approach to autonomous driving by decomposing actions into fundamental components, enhancing explainability, generalizability, and robustness to noisy input, and enabling the integration of existing ADAS functions into full autonomous driving systems without relying on specific hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive sensors and specialized infrastructure are integrated into the road network, then detection precision and localization accuracy improve, but system cost and geographical accessibility worsen
Solution Approach 1:
The patent creates virtual copies of physical objects through perception fields. Instead of relying on expensive physical sensors to detect every object, the system generates virtual force field representations that copy the essential characteristics of road objects, enabling detection and interaction through software-based virtual models rather than hardware-dependent physical detection
Solution Approach 2:
The patent replaces mechanical sensor systems with a software-based perception field system. The virtual force fields substitute for physical sensor arrays, using computational models to represent and interact with the environment rather than relying on mechanical detection hardware, thereby reducing system complexity while maintaining functional capability
2Adaptability or versatility
If machine learning models are trained with vast amounts of data and computational resources, then handling of real-world situations improves, but computational cost and training time worsen
Solution Approach 1:
The patent changes the fundamental parameters of the perception system by introducing virtual force fields with defined physical characteristics (magnitude, direction, decay rate). This allows the system to adapt to different situations by modifying field parameters rather than retraining entire machine learning models, reducing computational resource requirements while maintaining versatility
Solution Approach 2:
The patent performs preliminary action by pre-defining the mathematical formulations and physical properties of perception fields during system design. This upfront configuration eliminates the need for extensive real-time computational training, as the virtual force field behaviors are predetermined through analytical models rather than requiring vast computational resources during operation
3Extent of automation
If current automated driving functions are joined together to achieve full autonomy, then automation level improves, but system complexity and integration difficulty worsen
Solution Approach 1:
The patent creates a universal perception field framework that can serve multiple automated driving functions simultaneously. The same virtual force field infrastructure supports object detection, collision avoidance, path planning, and navigation, eliminating the need for separate specialized systems for each function and reducing overall integration complexity
Solution Approach 2:
The patent merges multiple discrete automated driving functions into a unified perception field-based system. Instead of integrating separate ADAS modules (ACC, AEB, LCA), the system combines them into a single cohesive framework where virtual force fields provide a common representation and control mechanism for all driving tasks, simplifying the integration process
Data Source
AI summary
A method for field related driving, the method includes receiving content from an information source located outside of a vehicle; obtaining object information regarding one or more objects located within an environment of the vehicle; estimating, by using a neural network (NN), and based on the object information, one or more virtual fields of the one or more objects; and determining, based on the one or more virtual fields, a virtual force for use in applying a driving related operation of the vehicle, wherein the virtual force is associated with a physical model and representing an impact of the one or more objects on a behavior of the vehicle. At least one step of the obtaining, the estimating and the determining is impacted by the content.