Vehicle Gradient-Aware Route Control for Precise Autonomous Driving
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Solution Overview
Problem
Existing autonomous driving technologies face challenges in accurately setting and maintaining vehicle travel routes, particularly due to insufficient consideration of longitudinal and transverse gradients, leading to inconsistent vehicle control and navigation precision.
Innovation Solution
A vehicle control system that includes processors to receive and process information about longitudinal and transverse gradients, controlling driving speed, ADAS, and setting landmarks based on these gradients, and utilizes sensors to correct offsets for improved navigation and route accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous driving technology uses conventional route setting methods without gradient information, then the system complexity is low, but the navigation precision and route accuracy deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-acquiring gradient information (longitudinal and transverse gradients) for the travel route before the vehicle actually traverses it. This gradient data is obtained in advance from maps or servers and stored for subsequent use during autonomous driving, allowing the vehicle to proactively adjust its control parameters rather than reacting to terrain changes in real-time
Solution Approach 2:
Gradient information serves as an intermediary element that mediates between the navigation system and the vehicle control system. The longitudinal and transverse gradient data act as intermediate parameters that translate route geometry into actionable control instructions for the vehicle, enabling precise navigation without requiring direct complex sensing of terrain by the vehicle itself
2Manufacturing precision
If the vehicle controller adjusts driving parameters based on gradient information, then the route following accuracy is improved, but the control complexity increases
Solution Approach 1:
The system applies local quality by adjusting vehicle control parameters according to local gradient conditions at different sections of the route. The longitudinal gradient information is used to modify acceleration and braking forces locally, while transverse gradient information adjusts steering angles locally, allowing precise adaptation to specific terrain features without overcomplicating the overall control system
Solution Approach 2:
The control system changes parameters by dynamically adjusting driving speed, acceleration, steering angle, and other vehicle control parameters based on gradient information. The vehicle controller modifies these parameters in real-time according to the longitudinal and transverse gradient values, enabling accurate route following through parameter adaptation rather than structural complexity
3Measurement precision
If the system uses sensor data without offset correction, then the measurement process is simple, but the gradient measurement accuracy deteriorates
Solution Approach 1:
The system implements feedback by comparing sensor-derived gradient measurements with expected gradient values from map data or server information. The offset between measured and expected values is calculated and fed back to correct the sensor calibration, creating a closed-loop system that continuously improves measurement accuracy through comparison and adjustment
Solution Approach 2:
The system replaces direct mechanical sensing of terrain gradients with an alternative approach using sensor data combined with computational correction. Instead of relying solely on complex mechanical gradient sensors, the system uses accelerometers and other standard vehicle sensors with mathematical offset correction based on known route gradient information, substituting a simpler sensing mechanism with intelligent processing
Data Source
AI summary
Disclosed are a vehicle control system and a driving method of a vehicle using the vehicle control system. The vehicle control system includes a vehicle controller that controls driving of a vehicle and one or more processors that process data related to the driving of the vehicle, receive information about a longitudinal gradient and a transverse gradient of the vehicle from a server, and control a driving speed, and an Advanced Driver Assistant System (ADAS), and/or set a landmark, based on the information on the longitudinal gradient and the transverse gradient.