Autonomous Driving Constraints from Axle Load Distribution
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Solution Overview
Problem
Autonomous vehicles lack effective methods to optimize their driving strategies based on the mass distribution of loads, leading to inefficient fuel use, increased wear-and-tear, and potential safety risks due to conservative or dangerous maneuvers.
Innovation Solution
The implementation of mass distribution-informed optimization systems that identify sensor data from axles to determine mass distribution data, allowing for corrective actions such as route updates and handling maneuver limits to improve fuel efficiency and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use conservative driving maneuvers to ensure safety, then collision risk is reduced, but fuel efficiency deteriorates and wear-and-tear increases
Solution Approach 1:
The system dynamically adjusts driving maneuvers based on real-time mass distribution data from sensors. Instead of using fixed conservative maneuvers, the autonomous vehicle optimizes acceleration, braking, and steering actions according to the current load configuration, enabling safe yet fuel-efficient operation that adapts to changing conditions
Solution Approach 2:
The system changes operational parameters (speed, acceleration, steering angle) based on mass distribution parameters detected by sensors. By monitoring how mass is distributed across the vehicle and adjusting driving parameters accordingly, the system achieves optimal balance between safety and fuel efficiency for each specific loading condition
2Use of energy by moving object
If autonomous vehicles use aggressive driving maneuvers to improve fuel efficiency, then fuel consumption is reduced, but safety deteriorates and collision risk increases
Solution Approach 1:
The system dynamically selects between conservative and aggressive maneuvers based on real-time mass distribution data. When the vehicle is lightly loaded, more aggressive fuel-efficient maneuvers are permitted; when heavily loaded or unevenly distributed, the system automatically adopts more conservative safe maneuvers
Solution Approach 2:
The system adjusts driving parameters within an optimized range determined by mass distribution. Instead of fixed aggressive or conservative parameters, the system continuously adapts acceleration rates, braking forces, and speed selections based on sensor data about load configuration
3Ease of operation
If autonomous vehicles follow fixed driving routes regardless of load, then operational simplicity is maintained, but fuel efficiency deteriorates due to inability to optimize for mass distribution
Solution Approach 1:
The system dynamically generates and adjusts driving routes based on real-time mass distribution data from sensors. Instead of following fixed predetermined routes, the autonomous vehicle optimizes its path selection and routing decisions according to current load conditions, maintaining operational simplicity through automated adaptation
4Device complexity
If autonomous vehicles do not monitor mass distribution, then system complexity is reduced, but safety deteriorates due to inability to detect improper load distribution
Solution Approach 1:
The system uses quick sensor measurements to rapidly assess mass distribution without complex analysis. By using simple sensor data from axle-mounted devices to directly infer load conditions, the system achieves safe operation with minimal processing complexity
Solution Approach 2:
The vehicle self-monitors its own mass distribution using integrated sensors and uses this information to self-adjust its driving behavior. The system serves its own safety needs by autonomously detecting load conditions and adapting maneuvers without requiring external monitoring or complex control systems
Data Source
AI summary
A method includes identifying suspension stiffness data and suspension deflection data associated with corresponding distal ends of one or more axles of an autonomous vehicle (AV). The method further includes determining, based on the suspension deflection data and the suspension stiffness data, driving constraint data for traveling at least a portion of a route. The method further includes causing, based on the driving constraint data, performance of a corrective action associated with the AV during the traveling of the at least a portion of a route.