Longitudinal Planning System for Autonomous Vehicle Distance Control
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Solution Overview
Problem
Current autonomous vehicles are limited by the capabilities of memory and processors, which restrict their ability to effectively navigate through complex traffic scenarios, as they struggle to handle the vast number of variables and decisions required for safe and efficient operation.
Innovation Solution
The implementation of a longitudinal planning system that utilizes a network of sensors, including LIDAR, RADAR, cameras, and ultrasonic sensors, to gather data and process information for autonomous decision-making, allowing the vehicle to optimize velocity changes and maintain safe distances from other vehicles, while also considering factors like lane changes and road conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional subsystems with standard frame sizes and shapes are used, then manufacturing cost is reduced, but the ability to take advantage of new technology and power sources is limited
Solution Approach 1:
The vehicle frame is designed with adjustable and reconfigurable components that allow it to dynamically adapt to different power source configurations and technological integrations, moving away from fixed standard sizes and shapes while maintaining manufacturing efficiency through modular design approaches
Solution Approach 2:
The vehicle structure is divided into modular segments that can be independently configured to accommodate various new technologies and power sources, allowing customization without requiring complete redesign of the entire vehicle platform
2Extent of automation
If the vehicle processes a near infinite number of variables and factors, then autonomous navigation capability is improved, but processing technology requirements become prohibitively complex
Solution Approach 1:
The autonomous vehicle system divides the processing of near infinite variables into separate functional modules, each handling specific aspects of navigation and decision-making, thereby reducing the complexity burden on any single processor while maintaining comprehensive autonomous capability
Solution Approach 2:
The patent introduces intermediary processing layers that filter, prioritize, and pre-process data before it reaches the main decision-making processors, reducing the immediate computational burden while enabling the system to consider a vast number of variables and factors
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables autonomous vehicles to execute smooth and safe velocity change maneuvers, optimizing obstacle avoidance and maintaining optimal following distances, thereby enhancing safety and efficiency in traffic environments.
Implementation Method 1
The implementation of a longitudinal planning system that utilizes a network of sensors, including LIDAR, RADAR, cameras, and ultrasonic sensors, to gather data
Implementation Method 2
The implementation of a longitudinal planning system that utilizes a network of sensors, including LIDAR, RADAR, cameras, and ultrasonic sensors, to gather data
Implementation Method 3
The implementation of a longitudinal planning system that utilizes a network of sensors, including LIDAR, RADAR, cameras, and ultrasonic sensors, to gather data
Data Source
AI summary
Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.