Vehicle Perception Neural Networks With Scenario-Based Layer Skipping

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Solution Overview

Problem

Conventional vehicle perception systems face challenges in balancing computational power, processing time, and power consumption due to increased neural network depth and finer input scales, which are not optimally adapted to varying driving scenarios.

Innovation Solution

The method involves dynamically modifying the operation of neural networks by skipping a determined number of neural layers and adjusting input scales for image data based on driving scenarios, without altering trained weights, to optimize performance in different driving conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If neural network depth is increased to enhance perception accuracy, then measurement precision is improved, but use of energy and computational power increases

Engineering Contradiction:
Improvespatial perception accuracyVSAvoidpower consumption
Core Design Contradiction:
Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system dynamically adjusts neural network depth based on driving scenario complexity. During complex scenarios (e.g., urban environments with pedestrians and intersections), the full neural network depth is utilized to maximize perception accuracy. During simple scenarios (e.g., highway driving), fewer neural layers are activated to reduce computational load and power consumption. This dynamic adaptation resolves the contradiction by making neural network depth variable rather than fixed.

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The system changes the parameter of neural network depth according to driving conditions. By modifying the effective depth of the neural network based on scenario complexity, the system achieves high measurement precision when needed while minimizing energy consumption during routine operations. This parameter change approach allows the same hardware to deliver variable performance levels matched to actual requirements.

Inventive Principle:
Principle #35Parameter changes

2Reliability

If neural network depth is increased to improve perception reliability, then reliability is improved, but processing time increases

Engineering Contradiction:
Improveperception reliabilityVSAvoidprocessing time
Core Design Contradiction:
ReliabilityVSLoss of time

Solution Approach 1:

The system dynamically adjusts neural network depth based on driving scenario complexity. During complex scenarios (e.g., urban environments with pedestrians and intersections), the full neural network depth is utilized to maximize perception accuracy. During simple scenarios (e.g., highway driving), fewer neural layers are activated to reduce computational load and power consumption. This dynamic adaptation resolves the contradiction by making neural network depth variable rather than fixed.

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The system changes the parameter of neural network depth according to driving conditions. By modifying the effective depth of the neural network based on scenario complexity, the system achieves high measurement precision when needed while minimizing energy consumption during routine operations. This parameter change approach allows the same hardware to deliver variable performance levels matched to actual requirements.

Inventive Principle:
Principle #35Parameter changes

3Measurement precision

If finer input scales are used to enhance detection precision, then measurement precision is improved, but use of energy and computational power increases

Engineering Contradiction:
Improveobject detection precisionVSAvoidcomputational power
Core Design Contradiction:
Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies different input scales to different spatial regions of the image data based on their importance. High-resolution (fine scale) processing is applied only to regions containing critical objects or areas of interest, while other regions are processed at lower resolutions. This local quality approach ensures high detection precision for important elements while reducing overall computational power requirements.

Inventive Principle:
Principle #3Local quality

4Adaptability or versatility

If conventional fixed neural network operation is used, then device complexity is low, but adaptability to varying driving scenarios is poor

Engineering Contradiction:
Improvescenario-based adaptationVSAvoidneural network operation complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system dynamically adjusts neural network depth based on driving scenario complexity. During complex scenarios (e.g., urban environments with pedestrians and intersections), the full neural network depth is utilized to maximize perception accuracy. During simple scenarios (e.g., highway driving), fewer neural layers are activated to reduce computational load and power consumption. This dynamic adaptation resolves the contradiction by making neural network depth variable rather than fixed.

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The system changes the parameter of neural network depth according to driving conditions. By modifying the effective depth of the neural network based on scenario complexity, the system achieves high measurement precision when needed while minimizing energy consumption during routine operations. This parameter change approach allows the same hardware to deliver variable performance levels matched to actual requirements.

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentUS20260045076A1Driving scenario-based modifications for vehicle perception systems
Publication Date: 2026.02.12 TOYOTA MOTOR ENG & MFG NORTH AMERICA INC
  • US20260045076A1 patent drawing
  • US20260045076A1 patent drawing
  • US20260045076A1 patent drawing

AI summary

Embodiments provide systems and methods that tailor, based on driving scenario for a vehicle: (1) operation of a neural network (used to process image data obtained during the driving scenario) to skip a determined number of neural layers; and (2) input scale for image data provided to the neural network. In this way, systems and methods can scale computational power and efficiency for image processing tasks as needed based on the nature and relative complexity of different driving scenarios.