Driver Attentiveness-Based Collision Mitigation via CAN Bus Signals
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Solution Overview
Problem
Existing collision mitigation systems rely solely on detected objects and often require additional devices like cameras to assess driver attentiveness, increasing complexity and cost, and may not function properly if these devices malfunction.
Innovation Solution
A system that sets a driver attention level based on interaction with vehicle devices, automatically controlling the vehicle's brakes and speed to mitigate collisions by pre-filling brake lines, performing a brake jerk, and slowing the vehicle when the driver is deemed inattentive, using existing vehicle signals over the CAN bus without additional interior sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional devices like cameras and sensors are added to assess driver attentiveness, then driver attentiveness detection accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The system uses existing vehicle devices and signals (steering wheel, accelerator, brake pedal, turn signal, radio) that the driver already interacts with to determine attentiveness. These devices serve dual purposes: their primary function plus attentiveness monitoring, eliminating the need for separate monitoring devices.
Solution Approach 2:
Existing vehicle components are made multi-functional. The steering wheel, accelerator, brake pedal, and other devices not only perform their primary functions but also serve as sensors for detecting driver attentiveness through interaction patterns, thereby reducing the need for dedicated attention-monitoring hardware.
2Measurement precision
If additional devices like cameras and sensors are added to assess driver attentiveness, then driver attentiveness detection accuracy is improved, but cost increases
Solution Approach 1:
The system leverages existing vehicle infrastructure and signals already present in modern vehicles (CAN bus data from steering wheel, accelerator, brake pedal, turn signals, radio). By reusing these existing components and their data streams, the system avoids additional manufacturing costs for dedicated attention-monitoring hardware.
Solution Approach 2:
The system uses low-cost, readily available vehicle components and their existing signal streams rather than expensive specialized sensors. The approach treats existing vehicle data as a free resource for attentiveness monitoring, minimizing additional component costs.
3Reliability
If collision mitigation systems intervene automatically based on detected objects, then collision avoidance capability is improved, but unnecessary interventions increase when driver is attentive
Solution Approach 1:
The system continuously monitors driver attentiveness through interactions with vehicle devices and uses this feedback to dynamically adjust mitigation actions. When the driver is attentive, automatic interventions are suppressed or reduced. When inattentive, the system activates appropriate mitigation measures, creating a closed-loop control system that adapts to driver state.
Solution Approach 2:
The collision mitigation system transitions from static, object-based triggering to dynamic, state-based triggering. The system's response threshold and intervention level change based on the driver's real-time attentiveness state, allowing the same object detection to produce different outcomes depending on driver condition.
Data Source
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AI summary
Methods and systems for mitigating vehicle collisions. One system includes a processor configured to set a driver attention level to at least one of an attentive level and an inattentive level based on a driver's interaction with at least one device located within a vehicle. The processor is also configured to automatically pre-fill at least one brake line of the vehicle when the driver attention level is set to the inattentive level, automatically perform a brake jerk when the driver attention level is set to the inattentive level and a distance between the vehicle and a closest object detected around the vehicle is less than a first predetermined distance, and automatically slow the vehicle when the driver attention level is set to the inattentive level and a distance between the vehicle and a closest object detected around the vehicle is less than a second predetermined distance.