VRU Collision Warning Logic Using Map-Based Turn Prediction
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Solution Overview
Problem
Current vehicle systems fail to provide accurate warnings of potential collisions with vulnerable road users (VRUs) due to inadequate detection of blind spots, leading to desensitization from false positive alerts and increased risk in urban environments.
Innovation Solution
A VRU collision avoidance system utilizing sensors, processors, and map data to detect VRUs and generate timely warnings based on precise collision calculations, including REM map data and safety driving models to ensure compliance with regulations like ECE151.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional blind-spot monitoring systems provide warnings at all times before theoretical collision points, then the system meets regulatory requirements for VRU protection, but the system generates many false positive warnings that risk desensitizing drivers
Solution Approach 1:
The system performs preliminary analysis of road geometry and vehicle trajectory using map data before generating warnings. By pre-determining whether the vehicle is in a position where a turn could lead to collision, the system avoids generating false warnings while still meeting regulatory requirements for early warning when actually needed.
Solution Approach 2:
The system introduces map data as an intermediary layer between sensor detection and warning generation. This intermediary provides contextual information about road layout and vehicle trajectory, allowing the system to filter out false positives while maintaining sensitivity to real threats.
2Reliability
If the system warns drivers of potential VRU collisions in all scenarios, then VRU safety is maximized, but driver attention and trust are reduced due to excessive false alarms
Solution Approach 1:
The system applies different warning strategies based on local conditions - using map data and trajectory analysis to determine whether a specific situation warrants a warning. This localized approach ensures warnings are generated only when truly necessary, maintaining driver attention while protecting VRUs.
3Device complexity
If the system uses only sensor-based VRU detection, then the system is simpler to implement, but the system cannot distinguish between relevant and irrelevant collision scenarios
Solution Approach 1:
The system makes the sensor system multi-functional by combining it with map data processing and trajectory analysis capabilities. This universal approach allows the same system to both detect VRUs and determine whether detected VRUs pose a real collision risk, eliminating the need for separate systems.
Data Source
AI summary
Techniques are disclosed for reducing false positives for generating warnings to avoid potential collisions between a vehicle and vulnerable road users (VRUs). This is accomplished via an onboard vehicle safety system that uses crowdsourced map data to determine whether a vehicle is capable of performing a maneuver that results in a lateral shift of the vehicle (which may include a lane-shifting or turning maneuver) within a predetermined threshold time period. The ability for the vehicle to make the turning maneuver, among other driving scenarios, may be used to by the safety system to intelligently determine whether a warning or other action is needed to avoid a potential collision with a VRU. In this way, the occurrence and number of false warnings/interventions are minimized or at least reduced, leading to more attentive drivers and thereby improving VRU safety.