Autonomous Vehicle Path Planning for Passing Cyclists Safely
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in safely and efficiently interacting with bicyclists on mixed-traffic roadways due to the complexity and variability of real-world road environments, particularly when approaching from behind, which can result in serious injuries and fatalities.
Innovation Solution
An autonomous vehicle system equipped with an imaging device and processing circuitry that identifies cyclist passing situations, plans a path based on these situations, and utilizes a positioning system to determine safe passing maneuvers, mimicking human driving behavior to maintain distance and navigate through various road conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use imaging devices and positioning systems to identify cyclist passing situations and plan paths, then safety of passing cyclists is improved, but device complexity increases
Solution Approach 1:
The system segments the cyclist passing situation into distinct categories (overtaking, lane sharing, road sharing) based on relative position and road conditions. This segmentation allows the complex problem of cyclist interaction to be broken down into manageable scenarios with specific path planning rules for each, improving safety without requiring the system to handle all possibilities as a single complex problem.
Solution Approach 2:
The system changes parameters such as lateral offset distance, passing speed, and approach distance based on the identified cyclist passing situation. For example, maintaining a minimum lateral offset of 1.5 meters during overtaking, or adjusting approach distance based on road type. These parameter adjustments enable safe passing behavior adapted to different scenarios without requiring complex real-time decision-making algorithms.
2Reliability
If the autonomous vehicle maintains a safe distance from cyclists during passing, then cyclist safety is improved, but passing time increases
Solution Approach 1:
The system dynamically adjusts the lateral offset distance and passing speed based on the identified cyclist passing situation and real-time road conditions. Rather than maintaining a fixed conservative distance, the system optimizes the distance and speed within safe parameters, allowing faster passing when conditions permit while still ensuring cyclist safety. This dynamic adjustment resolves the contradiction between safety and time efficiency.
3Measurement precision
If the autonomous vehicle system identifies various cyclist passing situations using multiple sensors, then identification accuracy is improved, but measurement precision requirements increase
Solution Approach 1:
The system uses a multi-functional sensor suite where imaging devices, positioning systems, and other sensors serve multiple purposes. For example, the imaging device not only detects cyclists but also identifies road markings, traffic signs, and environmental conditions. This multi-functionality allows accurate cyclist passing situation identification without requiring dedicated specialized sensors for each measurement, reducing overall system complexity while maintaining high identification accuracy.
Data Source
AI summary
An autonomous vehicle configured to autonomously pass a cyclist includes an imaging device and processing circuitry configured to receive information from the imaging device. Additionally, the processing circuitry of the autonomous vehicle is configured to identify a cyclist passing situation based on the information received from the imaging device, and plan a path of an autonomous vehicle based on the cyclist passing situation. The autonomous vehicle also includes a positioning system and the processing circuitry is further configured to receive information from the positioning system, determine if the cyclist passing situation is sufficiently identified, and identify the cyclist passing situation based on the information from the imaging device and the positioning system when the cyclist passing situation is not sufficiently identified based on the information received from the imaging device.