A Proactive System to Anticipate and Store the Damage Information on a Parked Car Using Machine Learning Algorithm Mercedes-Benz Research and Development India

Format:

Book

Conference/Event

Author/Creator:

Debnath, Sarnab, author.

Contributor:

Belur Subramanya, Sheshagiri

Govinda, Shiva Prasad

Patil, Prasad

Conference Name:

WCX SAE World Congress Experience (2025-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Vehicle ADAS Systems majorly comprises of two functions: Driving and Parking. The most common form of damage to the vehicle which goes unnoticed with unidentified cause are parking damages. A vehicle once parked at a certain location may get damaged without knowledge of the user. In this work developed a solution that not only pre-warns the driver but also prepares the vehicle beforehand if it suspects a damage may occur. This eliminates the latency between damage and information capture, detects small damages such as scratches, classifies the type of damage and informs the user beforehand. This is solution is different from our competitors as the existing solutions informs the user about the scratches/damages, but these solutions are expensive, have high response time, and the damage information is captured after the damage has occurred. The solution consists of the following check blocks: Precondition, Sensor Control and Action Module. The Precondition Module observes the vehicle parking location and GPS data to inform the driver about the parking area's accident history, enhancing pre-warning capabilities. It also blocks ambient noise using Active Band Pass filter along with Sliding Window FFT Algorithm for effective recording of vehicle damage noises. The Sensor Control block uses Ultrasonic and IMU sensors to sense presence of human being/ object within a certain threshold limit which is calculates a Risk Factor based on the Distance, Velocity and Acceleration of the approaching object. If this threshold limit is crossed, the vehicle opens its camera and microphone to start recording. The Action block then classifies the type of Damage Detected as a Minor or Major using sensor fusion techniques of IMU, Microphone and Camera Data. This is fed to the Robust CNN Machine Learning Algorithm which classifies the damage and extent and informs the user using proprietary application including images. This proactive approach offers significant improvements over existing solutions, providing a robust mechanism to protect parked vehicles from unnoticed damages

Notes:

Vendor supplied data

Publisher Number:

2025-01-8211

Access Restriction:

Restricted for use by site license