Ideaz

What the app does (high level)

Runs on the user’s smartphone (Android + iOS).

Continuously (but efficiently) monitors sensors while the phone detects driving (activity recognition).

If a likely crash is detected it: (a) opens an on-screen alert to let the user cancel, (b) if no cancel, automatically sends location + crash metadata to an emergency dispatch service and to the user’s emergency contacts, and (c) calls local emergency services / a partnered call center that notifies police/ambulance.

Optionally collects additional context (photos, video clip, sensor logs) to help responders and insurers.
This is how OnStar, Apple Crash Detection and several apps work.
onstar.com
+2
Apple Support
+2

2) How crash detection works (practical details)

Inputs you can use

Smartphone accelerometer & gyroscope (sudden high g-forces, rotation).

GPS speed + sudden deceleration.

Microphone (loud impact sound) — optional, privacy-sensitive.

Activity recognition / vehicle presence (to ignore walking/running).

Car data via Bluetooth OBD-II / CAN adapter for brake/fault signals (if the user has adapter).
Research + prototypes show smartphone sensors can detect accidents reliably when combined intelligently.
cs.wm.edu
+1

Detection logic (two layers)

ML-based classifier (reduces false positives): train a lightweight model that uses accelerometer/gyro/GPS + context (time of day, phone orientation, recent speed) to distinguish crashes vs. potholes / hard braking / dropping phone. Use supervised data from crash logs + synthetic data from simulator/bench tests. Patents and papers exist for these hybrid approaches.
Google Patents
+1

User confirmation flow

Immediately show an alert: “We detected a possible crash — call emergency services?” with a big Cancel button and a visible timeout . If user cancels, stop. If not, escalate automatically. Apple/Noonlight show similar flows.
The Verge
+1

3) System architecture & data flow

Device layer (phone): sensor collection, local inference, UI for alerts.

Backend / Cloud: receive verified crash reports, store incident logs, provide API for responder integrations, notify emergency contacts.

Dispatch / Call center (critical): partner with a 24/7 monitoring center (or build your own). The monitoring center verifies incidents and calls local emergency services / police / ambulance with location & incident severity. Many services do this rather than calling 911 directly from the app to avoid false alarms and to handle language/triage.
OtoZen
+1

Third-party integrations: SMS/voice gateways, local 112/911 APIs where available, ambulance dispatch APIs (if region supports), insurance portals, vehicle telematics (optional).

Data sent on escalation

GPS coordinates (lat/long), timestamp, severity estimate, vehicle heading/speed, sensor snippet (accelerometer/gyro), optionally photo/video/video-snapshot, user profile (name / medical info / emergency contacts). Keep payload small to deliver fast.

4) Legal, privacy & compliance (must-haves)

User consent for continuous sensor monitoring and for sharing location/health info. Provide clear in-app onboarding.

GDPR / local privacy laws: store minimal data, provide deletion and export options. Encrypt data in transit and at rest.

Medical info opt-in: only share allergies / meds if user explicitly enters it.

False alarms & liability: include terms that define what the app does; partner with official dispatchers to reduce unnecessary 1-9-1 calls.

Local emergency call rules: in many countries only citizens can directly call emergency numbers from apps; best practice is to send data to a certified dispatch center that then calls local services. OnStar/other operators use certified monitoring centers.
onstar.com
+1

5) MVP roadmap (practical step-by-step)

Phase 0 — Research & prototyping

Collect sample sensor data: normal driving, braking events, potholes, collisions (simulate safely), using phones mounted in cars. Use public datasets/papers for initial models.
cs.wm.edu
+1

Phase 1 — Minimal viable app

Platforms: Android first (more sensor access), then iOS.

Features: background activity recognition, crash-detection rule-based engine, local alert UI with cancel; on-confirm send SMS + GPS to emergency contact + simple cloud log.

No direct police integration yet — use SMS & phone calls to emergency contacts.

Phase 2 — Monitoring center & integrations

Partner with a 24/7 dispatch/monitoring service (or build) that accepts incident webhooks, calls local EMS/police, and tracks response. Add voice channel so the dispatcher can call the user automatically.
OtoZen

Phase 3 — Advanced

ML model to reduce false positives, OBD-II / CAN adapter integration (brake faults, airbag deployment), video/photo capture on event, insurer partnerships.

Multi-user/fleet features for commercial users.

6) Tech stack & components

Mobile: Kotlin (Android) / Swift (iOS) or React Native / Flutter if you want cross-platform but native gives best sensor access.

Local processing: small rule engine + TensorFlow Lite or Core ML for on-device ML.

Backend: Node.js / Python (FastAPI) + PostgreSQL; or serverless (AWS Lambda + DynamoDB) for rapid scaling.

Real-time: Webhooks + push notifications (Firebase, APNs).

Dispatch integration: REST API + SIP / telephony (Twilio, Plivo) to call emergency contacts or call centers.

Security: TLS, JWT for auth, encrypted PII storage.

7) False positives — how to minimize them

Require multiple signals: a high-G event and sudden speed loss and activity=driving.

Use context: phone mounted vs in pocket (orientation), road type (GPS speed), time-of-day heuristics.

Provide quick cancel UX and a reversible escalation (dispatcher calls before sending ambulance if appropriate).

Improve with ML retraining from labelled user events.

8) UX and user flows (must be simple)

Onboarding: request permissions, set emergency contacts, add medical info, show how detection works.

During drive: minimal battery usage, low-notification background mode.

Crash alert: large cancel button, 20–30 second countdown, auto-call if not canceled.

After event: show incident summary, allow editing, share with insurer/family.

9) Partnerships & go-to-market

Monitoring centers: contract with a certified 24/7 monitoring provider (many exist globally).
OtoZen

OEMs / telematics providers: later integrate with manufacturers or OBD-II dongles for richer data. OnStar and others show the value of embedded vehicle sensors.
onstar.com

Insurance companies: offer the app as a safety feature for discounts.

Ambulance/Police APIs: where available, integrate directly for automated dispatch.

10) Monetization options

Freemium: basic crash detection & emergency contacts free; premium adds monitoring center + dispatch, verified incident reports, video uploads, family sharing. (Noonlight and others use a subscription model.)
Teen Vogue

B2B: sell fleet/enterprise plans to logistics companies.

Partnerships with insurers for reduced premiums or co-marketing.

11) Benefits / positives (why this helps)

Faster emergency response → fewer deaths and better outcomes.

Works in older cars without built-in telematics (smartphone-based).

Provides evidence (location, sensor logs, video) for rescue and insurance claims.

Peace of mind for family members when loved ones drive alone. Real-world services (OnStar, Apple Crash Detection, third-party apps) have demonstrated this value.
onstar.com
+2
Apple Support
+2

12) Challenges & risks

False alarms → unnecessary dispatch costs and user fatigue. Mitigate with multi-signal detection, cancel UX and monitored escalation.

Privacy concerns over continuous monitoring — minimize retention and be transparent.

Regional emergency call rules and integration complexity — partner with local dispatch centers.

Liability — legal review & terms of service required.

13) Example minimal feature list to start building today

Background driving detection (activity recognition).

Rule-based crash detection (accelerometer + GPS speed drop).

In-app cancel alert with countdown.

Automatic SMS / location to emergency contacts + cloud incident log.

Admin dashboard (simple) listing incidents and status.

Once this is stable, add a paid 24/7 monitoring partner to make real dispatches.

14) Useful references & real-world examples

OnStar Automatic Crash Response (embedded telematics model).
onstar.com

Apple Crash Detection documentation (device-based detection and auto emergency call).
Apple Support

Academic papers / WreckWatch on smartphone crash detection algorithms.
cs.wm.edu
+1

Noonlight / other safety apps that implemented crash-to-911 behavior and subscription models.
Teen Vogue

Quick next steps you can take right now

Prototype: build an Android prototype that logs accelerometer/gyro/GPS and triggers an in-app alert on threshold breach. (Use a mounted phone during controlled hard braking tests.)

Collect labeled data: gather sample logs for normal driving, hard braking, potholes, and simulated impacts. This is the most valuable asset for tuning.

Contact monitoring centers: talk to 1–2 24/7 monitoring services for integration & pricing.

Legal review & privacy plan: draft consent screens and data retention policies.

If you want, I can:

produce a detailed MVP spec (API endpoints, DB schema, screen mockups),

write sample Android code that reads accelerometer/GPS and triggers the cancelable alert,