Vehicle Object Detection and Passenger Notification System
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Solution Overview
Problem
There is a risk of passengers leaving personal items or cargo behind in vehicles, regardless of whether they are autonomous, semi-autonomous, or manually controlled, due to distractions or lack of attention from drivers.
Innovation Solution
A system that uses a combination of sensors such as cameras, weight sensors, pressure sensors, and machine learning algorithms to detect and classify objects within the vehicle, providing audio, visual, and tactile reminders to passengers as they approach their destination, and escalating notifications if the items are not retrieved, with the option to contact the passenger or authorities if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If passengers are distracted or lack attention, then they may leave items behind, but implementing a detection system increases device complexity
Solution Approach 1:
The detection system is segmented into multiple independent sensor components (cameras, weight sensors, pressure sensors) distributed throughout the vehicle interior. Each sensor monitors specific zones independently, and their data is processed separately before being integrated by the controller. This modular segmentation reduces overall system complexity while maintaining high reliability through distributed monitoring.
Solution Approach 2:
The detection system is designed with multi-functionality to monitor various types of objects (personal items, cargo, trash) across different vehicle zones (seats, floor, compartments) using a single integrated system. The controller can identify and classify different object types and trigger appropriate notification sequences, making the system universally applicable to multiple monitoring needs without requiring separate dedicated systems for each function.
2Measurement precision
If multiple sensors and algorithms are used to detect objects, then detection accuracy improves, but system complexity increases
Solution Approach 1:
Multiple sensor types (cameras, weight sensors, pressure sensors) are merged into a single integrated detection system controlled by one controller. The controller combines data from all sensor sources and uses machine learning algorithms to synthesize this information into accurate object identification and classification. This merging approach achieves high detection precision through data fusion while managing complexity through centralized processing.
Solution Approach 2:
The controller acts as an intermediary between the multiple sensors and the notification system. It receives raw data from various sensors, processes this information through machine learning algorithms to identify and classify objects, and then triggers appropriate notifications. This intermediary processing layer manages the complexity of multiple sensors by providing a unified interface and intelligent data synthesis.
3Reliability
If notifications are provided at multiple stages, then item retrieval is ensured, but loss of time occurs due to multiple alerts
Solution Approach 1:
The system provides preliminary notifications at multiple staged intervals before the passenger exits the vehicle. First notifications are provided when the vehicle approaches the destination, followed by additional notifications if items are still detected. This preliminary action sequence ensures passengers are reminded in advance, allowing them to retrieve items before leaving, thereby maintaining high retrieval reliability without causing time loss from post-exit discoveries.
Solution Approach 2:
The notification system operates with feedback loops that monitor whether items have been retrieved between notifications. The controller continuously checks sensor data to determine if detected items are still present, and only escalates notifications if items remain. This feedback mechanism ensures reliable item retrieval by adapting notification intensity to actual retrieval status, avoiding unnecessary time loss from redundant alerts after items have been collected.
Data Source
AI summary
A system for detecting a classifying objects in a vehicle can comprise one or more sensors for detecting the presence of an object, other than a passenger, on the vehicle. The system can classify the object as, for example, a personal object or trash. The system can provide a series of escalating reminders for the passenger to take the object with them when they exit the vehicle. When a personal object is left in the vehicle after the passenger has exited the vehicle, the system can communicate with a computing device of a central control or the passenger using additional communication channels such as, for example, e-mail, text, or the Internet. When trash is left in the vehicle, the vehicle can automatically contact, or return to, a maintenance facility for servicing. This can prevent the use of unclean vehicles for new passengers, among other things.