Self-Driving Vehicle Path Speed Limits Near Occluding Objects
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Solution Overview
Problem
Self-driving vehicles face safety issues due to occluding objects that obstruct their field of view, leading to potential collisions with pedestrians or bicycles emerging from occluded areas, as they cannot sense these objects in advance and fail to predict collision risks.
Innovation Solution
A vehicle control method and apparatus that determine a hard brake speed limit point and potential collision point based on the vehicle's planned path and occluding object position, calculating a speed limit value to control the vehicle's speed and prevent collisions by filtering out irrelevant objects and performing appropriate speed control strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the vehicle maintains high speed to improve productivity, then the travel efficiency is improved, but the collision risk increases when occluding objects are present
Solution Approach 1:
The system performs preliminary detection of occluding objects and calculates speed limit values before the vehicle reaches critical zones. By identifying occluding objects in advance and pre-calculating safe speed limits based on their positions and the vehicle's braking characteristics, the system enables the vehicle to maintain higher speeds in safe conditions while automatically reducing speed when occlusion is detected, thus resolving the contradiction between travel efficiency and collision risk.
2Reliability
If the vehicle reduces speed to improve safety when occluding objects are detected, then the collision risk is reduced, but the travel efficiency decreases
Solution Approach 1:
The system applies speed control locally and selectively rather than globally. It identifies specific occluding objects that pose collision risks and calculates speed limit values only for those scenarios. The vehicle maintains its planned high speed in areas without occluding objects or where the occluding object does not pose a risk, while reducing speed only in specific zones where occlusion is detected and the calculated speed limit is below the planned speed. This localized approach minimizes the impact on overall travel efficiency while ensuring safety in critical areas.
3Reliability
If the vehicle increases detection range to improve safety coverage, then the ability to detect occluding objects is improved, but the computational complexity increases
Solution Approach 1:
The system dynamically adjusts detection parameters based on the vehicle's state and environment. It focuses computational resources on detecting occluding objects within relevant distance ranges and angular sectors that pose actual collision risks. The detection algorithm prioritizes identifying objects that meet specific criteria (size, position relative to planned path, movement characteristics) rather than processing all detected objects uniformly. This parameter-based filtering reduces computational complexity while maintaining comprehensive safety coverage for critical scenarios.
Data Source
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AI summary
This application discloses a vehicle control method, a vehicle control apparatus, an electronic device and a self-driving vehicle, and relates to the field of self-driving and intelligent transportation technologies. The method includes: when a vehicle is moving, in a case that an occluding object is detected, determining a hard brake speed limit point and a potential collision point according to a planned path of the vehicle and position information of the occluding object; calculating a speed limit value of the hard brake speed limit point based on a distance between the hard brake speed limit point and the potential collision point; in a case that a planned speed of the vehicle at the hard brake speed limit point is less than or equal to the speed limit value, controlling the vehicle to move at the planned speed. This application can predict a potential risk of collision of a moving vehicle as a result of occlusion of an occluding object, and improve the safety of vehicle autonomous driving.