Ride Recall: An Aftermarket Item-Reminder System for Shared & Rented Cars
Designed an In-car Object & Belongings Detection System for lower-level automation vehicles to help users avoid forgetting personal items
Presented at the Adjunct Proceedings of the 17th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Australia
Second Prize winner at Mensch und Computer 2025, Chemnitz, Germany
My Role and Responsibilities
Strategic Manager & Concept Developer
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User Research
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User Flow
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User Journey Mapping
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User Study Design
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User Testing Moderation
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Data Analysis
Project Context
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March 2025 – July 2025
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Team: University Project Team
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Project Team: 9 Members
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Role: Strategic Manager & Concept Developer
Tools used
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Figma
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Google Suite
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LaTeX
The Problem
Users of shared or rented vehicles often leave personal items behind, and existing approaches provide no reliable way to remind them or help recover those forgotten belongings. There is a need for an intelligent reminder system that detects and notifies users of items they may have forgotten in a vehicle, reducing the inconvenience and loss associated with item abandonment.
How might we help users remember and recover personal items left behind in shared or rented vehicles in a seamless and user-friendly way?
Design Process Overview
Primary & Secondary User Research
Research on Existing Driver & Occupant Monitoring Systems (DMS & OMS), and In-Cabin Sensor Technologies
Passenger Seat:
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The passenger seat group studied how current in-cabin monitoring systems observe driver and front passenger behavior to enhance safety and comfort.
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They focused on technologies such as infrared (IR) cameras, eye-tracking sensors, and facial recognition to detect states like distraction, fatigue, and attention.
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Examples like Subaru DriverFocus and Bosch systems were analyzed to understand how sensors are positioned (e.g., dashboard-mounted IR cameras) and how AI interprets visual data for real-time alerts.
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The group also explored different sensor types usable in this area, including near-infrared cameras, depth sensors, and motion detectors, evaluating their effectiveness under varied lighting and movement conditions.
Rear Seat:
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The rear seat group focused on monitoring passengers in the back seat, especially for safety-related applications like child presence detection.
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They researched systems using ultrasonic sensors, millimeter-wave radar, seat pressure mats, and thermal imaging to identify occupant presence and movement.
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Key case studies included Hyundai’s Rear Occupant Alert, which uses motion detection after the vehicle is off, and Volvo’s radar system, capable of sensing breathing patterns.
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This group also assessed how sensor technologies perform in rear seat environments, noting challenges such as occlusion and detecting small or motionless occupants.
Our data sources included:
Key Observations & Research Gaps
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Our research was grounded on two key pillars:
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Sensor and System Technologies
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Applications, Services, and Conceptual Innovations
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We reviewed the latest scientific literature from the past 5 years using databases like Google Scholar, focusing on cutting-edge developments in both these areas.
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To understand industry and regulatory expectations, we analyzed relevant roadmaps and regulatory frameworks such as Euro NCAP, which influences safety standards for vehicle monitoring systems.
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This comprehensive approach helped us gather insights into:
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The evolution and current state of sensor technologies and monitoring systems in vehicles.
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Emerging applications and innovative services beyond safety, including convenience features and future concepts.
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Our combined sources ensured a well-rounded understanding of both technical possibilities and market/regulatory demands, shaping our project’s direction effectively.
​Observations:
Current in-cabin systems mainly focus on safety use-cases like driver attention or child presence, with limited support for comfort, personalization, or post-exit monitoring.
Monitoring is mostly active during vehicle occupancy; post-exit functions and multi-functional uses are limited.
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Research Gaps:
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Standards and regulations for occupant monitoring systems exist for safety, but there's a noticeable gap when it comes to features like comfort automation or health tracking.
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High costs of advanced sensors and technical complexity pose adoption barriers, especially for mid-range vehicle platforms.
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User acceptance is challenged by discomfort with constant monitoring, potential misuse of data, and unclear benefits in daily scenarios.
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Sensor technologies like infrared, ultrasonic, radar, and thermal are mature but often underused in integrated or multifunctional systems.
Research Insights
​Insights:
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Research showed that while sensor capabilities are technically available, their usage is still narrow, leaving untapped potential in areas like gesture control, personalized comfort, and wellness tracking.
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Many existing gaps relate not only to hardware limitations but also to social and regulatory factors like user trust, data handling transparency, and unclear standards.
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Several emerging concept directions—such as passenger drowsiness detection, object and belonging tracking, ergonomic adjustments, and adaptive comfort systems—reflect opportunities where technology could meet real user needs.
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Transition:
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Building on these research findings and gaps, the next phase focuses on developing and evaluating conceptual designs for advanced in-cabin monitoring and control systems.
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Our goal is to identify concepts that balance technical feasibility, user needs, and potential impact for future vehicle cabins.
Personas and User Journey
Concept Development
​To strike a balance between cooperation and innovation, we split into two groups of five. Each team developed 3 concepts.
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Team A focused on the passenger seat
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Team B focused on the rear seats.
Team A
Team B
Overview of Concepts
Decision Process
The Collected concepts where analyzed based on a Weighted Sum Model
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The Automated Ergonomics and safety adjustment was best overall
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Item Detection, the second best, was picked out of feasibility concerns
It might be interesting for someone with more resources to look into concept one again
Object and Belonging Detection
This Object & Belongings Detection System is a smart in-car feature designed for lower-level automation vehicles to help users avoid forgetting personal items like bags, phones, or laptops in the rear seat.
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It’s especially useful for parents, pet owners, and busy passengers who may forget important belongings. By detecting objects and sending alerts, it reduces the chances of lost items and improves overall safety. The system also supports scenarios like child or pet presence.
Functionality and benefits​
The following functions are to be implemented in order to realize the concept:
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Smart Detection System
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Personalized Memory & Alerts
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Quick Check & Zone-Based Scanning
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Customizable Safety Modes
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Voice-Controlled Assistance
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Benefits of these functions:
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Detects forgotten items, children, or pets left behind in the vehicle
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Learns user habits and sends reminders for frequently carried items
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Quick Check & Zone-Based Scanning
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Adjusts detection settings based on passenger types (like kids or pets)
Target User Group & User Interface
​Target User Groups:
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Parents with young children
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Pet owners
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Elderly passengers
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Frequent travellers or Business Professionals
User Interface:
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Voice Control
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Audio Alert / Speaker Notification
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Heatmap (Smart zones)
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Mobile App Sync UI
Sensor Setup and System Requirements
Sensor Setup:
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Weight sensors
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Motion sensors
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Thermal Camara
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Memory/object recognition Ai
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Capacitive Zone Mats
System Requirements:
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Hardware: Rear seat camera + optional IR camera for low-light, Low-cost weight/motion sensor arrays in seat and floor, Audio system for voice UI
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Software: Object detection via onboard AI (e.g., YOLOv4-lite or OpenVINO), Alert logic, API to connect with companion app and cloud, Learning model to personalize frequently used/forgotten objects
Initial Interviews
Questions:
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What or who do people usually transport?
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How often do they travel?
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Where do they go?
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Insights:
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Most participants travel daily for work, errands, or pet visits.
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Items like laptops, water bottles, chargers, sunglasses, and medicines are commonly carried.
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Routines vary — some include carrying pets or specific work tools.
Questions:
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Where are people and things usually placed?
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Which items are left behind intentionally vs. accidentally?
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How frequently do people forget things in their car?
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Insights:
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Items are often placed in dashboards, drawers, rear seats, or the front seat (used intentionally for visibility).
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Commonly forgotten items: chargers, water bottles, small personal items — remembered when needed.
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Forgetting is linked to being in a hurry or breaking routine.
Questions:
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What kinds of reminder systems do users prefer?
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Insights:
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Users are open to apps with notifications (on screen + mobile).
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Suggestions include voice assistants, exit-based alerts, and customizable item tracking.
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Privacy matters — cameras are distrusted, but sensor-based solutions are more accepted.
Prototype Implementation
AI Prototype
What can the current code do?
Power & Installation
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GoPro Image:
- Go pro is connected to the laptop via wifi
- The program takes a picture via the GoPro
- It then saves the image in the test folder
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AI Output:
- Training results
- Classification of the image in the test folder
- Sending the image by e-mail
Mobile App
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Once the user exits the vehicle and the system detects a departure (e.g., moving >5m away), a notification is 
triggered shortly after the end of the rental or shared 
car session.
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Additionally, a dashboard displays past rental details, time and location of exit, and any flagged forgotten items, allowing users to review and manage alerts across multiple trips. This supports post-handover awareness, especially when transitioning between vehicles under time pressure.
​Detection:
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In the app, users can see an image for back seat based on camera detection 
or schematic of the cabin that highlights any detected items for items detected using the weight sensor
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The user can quickly respond by choosing “Mark as Safe,” “Remind Me Later,” or “Ignore.”
​Block Car:
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This is a safety feature that temporarily blocks the the vehicle in the rental system to prevent the next handover until the user confirms item retrieval.
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Users can view trip summaries, exit time and location, detected objects, and any flagged vehicles, enabling better post-trip decision-making and item recovery.
​Safe, Remind and Ignore:
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The user can also perform other actions like: 

Safe – Confirms that the forgotten item is safe and the user knows about it. 

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Remind Me Later – Delays the notification for 15 mins
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Ignore – Dismisses the alert and marks the item as unimportant
User Study
Participant Groups
Sample Size
Total no. of participants: 12
Participants per group: 6
Demographics
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Each group will include participants with:
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Range of age groups
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Mix of gender
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Tech-savvy and non-tech users
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Casual car renters simulating short-term rental scenarios
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Study Setup
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Every participant experience the condition only once
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Between Subject Design
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Controlled environment mimicking a 2-day multi-stop trip with friends
Baseline Condition
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Control group completed tasks without system support, enabling comparison with the experimental group who received item detection alerts.
Experimental Group
Users encounter situations where pressure sensors or cameras detect forgotten items in the car and trigger alert notifications.
Control Group
This condition involves no detection system for identifying or notifying users about forgotten items in the vehicle.
Set-up
Research Questions:
RQ1: Does the system help users recover forgotten items and improve recall behavior?


RQ2: How do users perceive and respond to the system’s visual alerts in terms of clarity, trust, and usefulness?
Goal:
Evaluate how well Ride Recall supports users in recalling and retrieving forgotten items.
Design:
- Mixed-methods, scenario-based study
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Stationary vehicle used for controlled realism
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Wizard-of-Oz simulation: alerts delivered manually
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12 participants (6 baseline, 6 system)

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Conditions:
- Baseline group: No system support – relied on memory
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System group: Received alerts via smartphone during car return

Data Collection:
- Pre/post questionnaires (mental effort, recall confidence)
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UEQ-S (System Usability) for system group
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Short interviews (5–7 min) for subjective feedback
Data Collection
Notes
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Monitored participant behavior during in-car routines
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Noted item placement and instances of forgotten items
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Identified natural reminder strategies (e.g., placing items in view)
Pre-post questionnaire, UEQ-S
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Collected structured responses on frequency of forgetting items
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Measured user satisfaction with reminder concepts using UEQ-S
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Analyzed usage patterns and user preferences across participants
Short Interview
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Explored personal experiences with forgetting items in cars
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Discussed trust in reminder systems and privacy concerns
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Gained insights into user needs and preferred alert mechanisms
User Study Flow
Evaluation Results and Key Insights
Insights of the User Testing:
Post-test - Control Group v/s Experimental Group
Positive Impacts of the System:
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Ease of checking belongings (+1.00): Clear improvement. The system helps reduce the manual effort of scanning the car.
for usability and user comfort. -
Mental effort reduction (+1.33): Major cognitive load relief
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Increased confidence (+0.67): People felt more assured that they hadn’t forgotten anything.
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High Recommendation Potential:
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Participants in both groups showed high willingness to use and recommend the system, but slightly higher scores were seen with real usage.
Sensor Comfort Slightly Lower:
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A small drop in comfort with sensors (-0.50) among those who actually experienced them. This suggests that transparency or control over data/privacy might need to be addressed in future iterations.
Interview Analysis
Summary
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