Blind Spot Risk Estimation Using Passing Vehicle Behavior
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Solution Overview
Problem
Existing vehicle control systems cannot effectively manage risks associated with blind spots without relying on communication with preceding vehicles, especially when such communication is unavailable or the preceding vehicles lack necessary risk recognition systems.
Innovation Solution
A vehicle control method and controller that utilize in-vehicle sensors to recognize blind spots and observe the traveling behavior of nearby vehicles, estimating potential risks based on lateral position offsets and passing speeds, and adjust vehicle operations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If communication with preceding vehicles is used to acquire risk information, then risk recognition capability is improved, but system dependency on external communication increases
Solution Approach 1:
The ego-vehicle uses its own external sensors to independently detect and analyze the traveling behavior of blind spot passing vehicles, rather than relying on risk information transmitted from preceding vehicles. This self-service approach enables the system to autonomously estimate risks by observing lateral position offsets and passing speeds, eliminating dependency on external communication systems while maintaining reliable risk recognition capability
Solution Approach 2:
The patent introduces an intermediary observation mechanism where the ego-vehicle uses its external sensors to indirectly detect the behavior of blind spot passing vehicles. This intermediary approach allows the system to infer risk levels by analyzing the traveling patterns of other vehicles in the blind spot area, providing a reliable risk assessment pathway that does not require direct communication with preceding vehicles
2Measurement precision
If external sensors are used to observe blind spot passing vehicles, then risk estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The system creates a virtual model of the blind spot environment by copying and analyzing the traveling behavior data of blind spot passing vehicles obtained through external sensors. Instead of requiring complex dedicated blind spot detection hardware, the system replicates the observation capability using existing sensor data, processing lateral position offsets and passing speeds to generate accurate risk estimates without significantly increasing device complexity
Solution Approach 2:
The external sensors of the ego-vehicle are utilized for multiple purposes: primary functions such as obstacle detection and navigation, plus the secondary function of observing blind spot passing vehicle behavior for risk estimation. This multi-functionality approach allows the same sensor system to serve both routine driving operations and specialized blind spot risk assessment, avoiding the need for additional dedicated sensors and thereby limiting the increase in device complexity
Data Source
AI summary
According to the method of the present disclosure, first, a blind spot that is present in front of an ego-vehicle and cannot be seen from the ego-vehicle is recognized from information on a surrounding environment of the ego-vehicle. Next, a traveling behavior of a blind spot passing vehicle in the vicinity of the blind spot is observed from information acquired by an external sensor of the ego-vehicle. The blind spot passing vehicle is a vehicle that passes through the blind spot before the ego-vehicle. Then, a risk caused by the blind spot is estimated based on the traveling behavior of the blind spot passing vehicle. Finally, the ego-vehicle is operated in a mode corresponding to the risk.