Pedestrian Protection Deployment Using Active-Passive Crash Sensing
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Solution Overview
Problem
Current vehicle safety systems face challenges in accurately discriminating between pedestrian and vehicle collisions, particularly in situations where the severity of the impact is not immediately clear, leading to potential delays in deploying protective measures.
Innovation Solution
A vehicle safety system that integrates both passive and active components, utilizing a controller to analyze signals from crash sensors and active sensors like cameras, radar, and LIDAR to differentiate between pedestrian and vehicle collisions using hybrid crash discrimination metrics and safing metrics, allowing for timely deployment of pedestrian protection devices such as airbags or hood lifters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional crash sensors alone are used to determine collision type, then the system is simpler, but the accuracy in discriminating pedestrian vs. vehicle collisions is insufficient leading to delays in deploying protective measures
Solution Approach 1:
The patent combines active safety sensors (cameras, radar, LIDAR) with passive crash sensors to create an integrated sensing system. The active sensors detect objects and classify them as pedestrians or vehicles, while crash sensors detect impact forces. By merging these sensor types, the system achieves accurate collision type discrimination without relying solely on complex crash sensor analysis.
Solution Approach 2:
The system performs preliminary object classification using active sensors before the collision occurs. By identifying pedestrians in advance and positioning them in collision risk zones, the system prepares the discrimination logic ahead of time. When a crash is detected, the pre-established object classification immediately informs collision type determination, eliminating delays associated with post-collision analysis.
2Speed
If the system uses lower thresholds for crash discrimination to improve responsiveness, then pedestrian collision detection is faster, but false positives from non-pedestrian impacts increase
Solution Approach 1:
The patent introduces active sensor data as an intermediary element that mediates between crash sensor signals and deployment decisions. When crash sensors detect an impact, the system queries active sensors to retrieve pre-established object classification information. This intermediary information allows the system to use lower crash discrimination thresholds for pedestrian detection while maintaining reliability, because the active sensor data confirms whether the impacted object is indeed a pedestrian.
Solution Approach 2:
The system dynamically adjusts crash discrimination thresholds based on object type. For pedestrian collisions, lower thresholds are applied to enable faster detection, while for vehicle collisions, higher thresholds are used to maintain reliability. This dynamic threshold adjustment is made possible by the integration of active sensor object classification with passive crash sensor data.
3Measurement precision
If the system integrates multiple sensor types for comprehensive collision discrimination, then accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The system performs object classification and risk zone determination using active sensors before collisions occur. This preliminary processing establishes a ready database of object types and positions that can be immediately queried during a crash event. By moving the computationally intensive object classification work to the pre-collision phase, the system minimizes processing time during actual deployment decisions.
Solution Approach 2:
The system uses feedback from active sensors to guide crash discrimination logic. When crash sensors detect an impact, the system queries active sensors for object classification feedback. This feedback mechanism allows the system to focus processing only on relevant objects (those in collision risk zones) rather than analyzing all detected objects, thereby reducing computational load and processing time.
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
Enhances the responsiveness of the vehicle safety system by accurately determining the presence and proximity of pedestrians, enabling the deployment of protective measures in a manner that mitigates pedestrian collisions effectively, even in complex scenarios.
Implementation Method 1
Radar sensors use an echo system to detect objects, which is beneficial in case of poor visibility, which can detract from the camera's effectiveness. Radar sensors emit electromagnetic waves and receive the 'echo' that is reflected back from the surrounding objects.
Implementation Method 2
Lidar sensors also apply the echo principle, using laser pulses instead of radio waves. Lidar sensors record distances and relative speeds with an accuracy on par with radar.
Implementation Method 3
Passive safety systems include one or more sensors, such as accelerometers and/or pressure sensors, that are configured to sense the occurrence of a crash event.
Data Source
AI summary
A vehicle safety system for helping to protect a pedestrian in the event of a vehicle frontal collision with the pedestrian includes a plurality of crash sensors for sensing a vehicle frontal collision, an actuatable pedestrian protection device, a controller for controlling actuation of the pedestrian protection device, and at least one active sensor for determining the presence of a pedestrian in the path of the vehicle.