Absolutely. Below is a complete final-year/major-project style design and documentation for:

AI-Based Accident Prevention and Intelligent IoT Alert System Using NVIDIA Jetson Nano, ESP32, AI Agent, n8n, Telegram, Google Sheets and ThingSpeak

The design combines edge AI + ESP32 sensors + IoT + an n8n agentic workflow + cloud dashboard + Telegram voice alerts.

Important design point: ESP32 does not need to run a large language model itself. In this architecture, the ESP32 is the sensing/actuation device, while the AI Agent runs in n8n/cloud or on an available AI server. The Jetson Nano performs the computer-vision inference locally.

The NVIDIA Jetson Nano has a 128-core Maxwell GPU and supports CUDA, TensorRT and computer-vision/deep-learning workloads. For the Nano specifically, JetPack 4.6.6 is the final JetPack 4 release and supports Jetson Nano.

1. Project title

AI-Based Accident Prevention and Intelligent IoT Alert System Using NVIDIA Jetson Nano, ESP32, Agentic AI, n8n Automation and Telegram Voice Notifications

Short title

AI Accident Prevention & Agentic IoT System

Technologies

NVIDIA Jetson Nano
ESP32
USB/CSI camera
MPU6050 accelerometer/gyroscope
Ultrasonic sensor
GPS module, optional
Buzzer
LEDs
Relay/motor-control output, optional
Wi-Fi
Computer Vision
YOLO object detection
Python
OpenCV
TensorRT
MQTT/HTTP
n8n
AI Agent
Telegram Bot
Telegram voice notifications
Google Sheets
ThingSpeak
HTML/CSS/JavaScript
REST APIs

2. Abstract

The proposed system is an AI-powered accident prevention and emergency notification platform designed for vehicles, robots, industrial equipment or smart transportation applications.

The system combines an NVIDIA Jetson Nano for real-time camera-based AI analysis with an ESP32 for collecting physical sensor information such as acceleration, vibration, distance and optional GPS data.

The Jetson Nano processes the camera stream using an object-detection model. The ESP32 continuously measures sensor parameters. These two data sources are combined into a risk/fusion engine.

For example:



Vehicle detected
       +
Vehicle approaching obstacle
       +
High acceleration/deceleration
       +
Abnormal vibration
       +
Unsafe distance
       ↓
AI Risk Engine
       ↓
LOW / MEDIUM / HIGH / CRITICAL

When a dangerous situation is detected, the system sends the event to an n8n automation workflow.

n8n then acts as the orchestration layer:



Jetson/ESP32
     ↓
n8n Webhook
     ↓
Data Validation
     ↓
AI Agent
     ↓
Risk Interpretation
     ↓
 ┌───┼────────┬───────────┐
 ↓   ↓        ↓           ↓
Telegram Google   ThingSpeak Dashboard
Voice    Sheets
Alert

n8n's Webhook node can expose an HTTP endpoint for external devices and applications, while its Google Sheets and Telegram integrations can automate data logging and notifications.

ThingSpeak provides REST/MQTT interfaces for storing and visualizing IoT channel data.

3. Main objective

The primary objective is:

To develop an intelligent accident-prevention system capable of detecting dangerous situations using computer vision and physical sensors, locally assessing risk, and automatically notifying users through an AI-powered IoT automation system.

4. Secondary objectives

Detect vehicles, people and obstacles using AI.
Measure acceleration and vibration.
Measure distance from obstacles.
Detect sudden impact/deceleration.
Calculate an accident-risk score.
Prevent an accident when possible by giving an early warning.
Generate an emergency event when an accident is suspected.
Send sensor data to the cloud.
Store accident records in Google Sheets.
Display IoT data using ThingSpeak.
Generate intelligent explanations using an AI Agent.
Send Telegram notifications.
Send Telegram voice alerts for critical events.
Maintain an event history.
Provide a web dashboard.

5. Proposed system architecture

High-level architecture



                         ┌─────────────────────────┐
                         │       CAMERA            │
                         │ USB / CSI Camera        │
                         └────────────┬────────────┘
                                      │
                                      ▼
                         ┌─────────────────────────┐
                         │    NVIDIA JETSON NANO   │
                         │                         │
                         │ OpenCV                  │
                         │ YOLO Object Detection   │
                         │ TensorRT                │
                         │ Risk Detection          │
                         └────────────┬────────────┘
                                      │
                                      │ HTTP/JSON
                                      ▼
┌─────────────────┐          ┌──────────────────────┐
│     ESP32       │          │   n8n AUTOMATION     │
│                 │ Wi-Fi    │                      │
│ MPU6050         ├─────────►│ Webhook              │
│ Ultrasonic      │          │ Data Processing      │
│ GPS             │          │ AI Agent             │
│ Buzzer          │          │ Decision Engine      │
│ LED             │          │                      │
└───────┬─────────┘          └──────┬───────┬───────┘
        │                           │       │
        │                           │       │
        ▼                           ▼       ▼
 Local Warning                 Telegram   Google
 Buzzer/LED                   Voice       Sheets
                              Alert
                                  │
                                  ▼
                              User/Admin
                                  │
                                  ▼
                             ┌───────────┐
                             │ ThingSpeak│
                             │ Dashboard │
                             └───────────┘

6. Detailed block diagram



                  ┌─────────────────────────┐
                  │       VEHICLE           │
                  │ / ROBOT / TEST PLATFORM │
                  └───────────┬─────────────┘
                              │
              ┌───────────────┴────────────────┐
              │                                │
              ▼                                ▼
       ┌─────────────┐                  ┌──────────────┐
       │   CAMERA    │                  │    ESP32     │
       └──────┬──────┘                  └──────┬───────┘
              │                                │
              ▼                                │
       ┌─────────────┐                         │
       │ Jetson Nano │◄────────────────────────┘
       │             │      Sensor Data
       │ YOLO        │
       │ OpenCV      │
       │ TensorRT    │
       └──────┬──────┘
              │
              ▼
       ┌─────────────┐
       │ Risk Fusion │
       │ Engine       │
       └──────┬──────┘
              │
        ┌─────┴──────┐
        │            │
        ▼            ▼
   SAFE/WARNING    ACCIDENT
        │            │
        └─────┬──────┘
              ▼
       ┌─────────────┐
       │ n8n Webhook │
       └──────┬──────┘
              ▼
       ┌─────────────┐
       │ AI Agent    │
       └──────┬──────┘
              │
       ┌──────┼───────────┬────────────┐
       ▼      ▼           ▼            ▼
   Telegram Google     ThingSpeak   Web Dashboard
   Voice    Sheets

7. Hardware requirements

Required hardware

Component	Quantity	Purpose
NVIDIA Jetson Nano	1	AI processing
ESP32 DevKit	1	Sensor/IoT controller
Camera	1	Accident/obstacle vision
MPU6050	1	Acceleration + gyroscope
HC-SR04	1	Distance detection
Buzzer	1	Local warning
LED	2–3	Status indication
220 Ω resistor	2–3	LED protection
Breadboard	1	Prototyping
Jumper wires	As required	Connections
Wi-Fi	1	Internet communication
GPS NEO-6M	Optional	Location
Power supply	As required	Power

8. Optional hardware

For a more advanced version:

ESP32-CAM
GPS
OLED display
GSM/LTE module
Motor driver
Relay
Emergency button
Alcohol sensor
Temperature sensor
Heart-rate sensor
Steering sensor
Wheel-speed sensor

9. ESP32 sensor subsystem

The ESP32 collects:

MPU6050

Acceleration X
Acceleration Y
Acceleration Z
Gyroscope X
Gyroscope Y
Gyroscope Z

Ultrasonic

Distance to obstacle

GPS

Latitude
Longitude
Speed

Local warning

Buzzer
Red LED
Green LED

10. ESP32 schematic

A practical connection is:



                 ESP32
        ┌────────────────────┐
        │                    │
        │  GPIO 21 ──────────┼──── SDA
        │  GPIO 22 ──────────┼──── SCL
        │                    │
        │  GPIO 5 ───────────┼──── HC-SR04 TRIG
        │  GPIO 18 ◄─────────┼──── HC-SR04 ECHO
        │                    │
        │  GPIO 25 ──────────┼──── BUZZER
        │  GPIO 26 ──────────┼──── GREEN LED
        │  GPIO 27 ──────────┼──── RED LED
        │                    │
        └────────────────────┘
                  │
                  │ I2C
                  ▼
           ┌────────────┐
           │  MPU6050   │
           │            │
           │ VCC        │
           │ GND        │
           │ SDA        │
           │ SCL        │
           └────────────┘

Important HC-SR04 note

Do not directly connect a 5 V HC-SR04 ECHO output to an ESP32 GPIO. Use a voltage divider/level shifter so that the ESP32 receives a safe logic voltage.

11. Recommended ESP32 pin table

Device	ESP32 pin
MPU6050 SDA	GPIO 21
MPU6050 SCL	GPIO 22
HC-SR04 TRIG	GPIO 5
HC-SR04 ECHO	GPIO 18 through level shifting
Buzzer	GPIO 25
Green LED	GPIO 26
Red LED	GPIO 27
GPS RX	GPIO 16
GPS TX	GPIO 17

Pins can be changed in software.

12. Accident prevention concept

The system should not simply ask:

"Did an accident happen?"

It should ask:

"Is the current situation becoming dangerous?"

Therefore the system has four levels.

Level 0 — SAFE



Risk = 0–29

No action.

Level 1 — WARNING



Risk = 30–49

Local buzzer/LED warning.

Level 2 — HIGH RISK



Risk = 50–74

n8n receives event.

Telegram warning can be generated.

Level 3 — CRITICAL



Risk = 75–100

Emergency workflow.



Telegram text
+
Telegram voice
+
Google Sheets
+
ThingSpeak
+
Dashboard

13. Risk calculation

A simple explainable model is better for a college project than claiming that a random AI model directly "predicts accidents."

For example:



Risk Score =
     0.30 × Vision Risk
   + 0.25 × Acceleration Risk
   + 0.20 × Distance Risk
   + 0.15 × Speed Risk
   + 0.10 × Vibration Risk

Each component is normalized from 0–100.

Example:



Vision risk       = 80
Acceleration      = 70
Distance risk     = 90
Speed risk        = 60
Vibration         = 40

Risk =
0.30(80)
+0.25(70)
+0.20(90)
+0.15(60)
+0.10(40)

= 24 + 17.5 + 18 + 9 + 4
= 72.5

Therefore:



72.5 → HIGH RISK

14. Computer vision subsystem

The camera is connected to Jetson Nano.

The processing pipeline is:



Camera
   ↓
Frame Capture
   ↓
Resize
   ↓
YOLO inference
   ↓
Object Detection
   ↓
Vehicle/person detection
   ↓
Distance estimation
   ↓
Time-to-collision estimation
   ↓
Vision Risk

YOLO can detect classes such as:



person
car
motorcycle
bus
truck
bicycle
traffic light

15. Why Jetson Nano?

The Jetson Nano is suitable for edge AI because it provides GPU acceleration and interfaces including camera, GPIO, I²C, SPI and UART.

The recommended Nano software baseline is:



Jetson Nano
    ↓
JetPack 4.6.6
    ↓
Jetson Linux R32.7.6
    ↓
Ubuntu 18.04-based environment
    ↓
CUDA / cuDNN / TensorRT
    ↓
Python + OpenCV

JetPack 4.6.6 is the final JetPack 4 release, so this is an important consideration for a new project: the Nano is excellent for a prototype/academic project, but a new production design should consider a newer Jetson platform.

16. Jetson software installation

After setting up Jetson Nano:



sudo apt update
sudo apt upgrade

Install basic packages:



sudo apt install -y \
python3-pip \
python3-opencv \
git \
curl \
wget \
ffmpeg \
libopenblas-dev

Install Python packages:



pip3 install numpy
pip3 install flask
pip3 install requests
pip3 install paho-mqtt

Check Jetson:



sudo jetson_clocks

Check CUDA:



nvcc --version

Check GPU:



tegrastats

17. Project directory

Create:



accident-ai/
│
├── jetson/
│   ├── camera.py
│   ├── detector.py
│   ├── risk_engine.py
│   ├── sensor_client.py
│   ├── server.py
│   ├── config.py
│   └── main.py
│
├── esp32/
│   └── accident_sensor.ino
│
├── dashboard/
│   ├── index.html
│   ├── style.css
│   └── app.js
│
├── n8n/
│   ├── workflow-description.md
│   └── sample-payload.json
│
├── models/
│   └── model.onnx
│
└── README.md

18. ESP32 software

Below is a complete prototype ESP32 program.

It:

connects to Wi-Fi
reads MPU6050
reads ultrasonic distance
calculates acceleration magnitude
activates local warning
sends JSON to the n8n webhook



#include <WiFi.h>
#include <HTTPClient.h>
#include <Wire.h>
#include <MPU6050.h>
#include <ArduinoJson.h>

const char* WIFI_SSID = "YOUR_WIFI";
const char* WIFI_PASSWORD = "YOUR_PASSWORD";

const char* N8N_URL =
  "https://YOUR-N8N-DOMAIN/webhook/accident-sensor";

#define TRIG_PIN 5
#define ECHO_PIN 18

#define BUZZER_PIN 25
#define GREEN_LED 26
#define RED_LED 27

MPU6050 mpu;

unsigned long lastSend = 0;
const unsigned long SEND_INTERVAL = 5000;

float readDistance()
{
  digitalWrite(TRIG_PIN, LOW);
  delayMicroseconds(2);

  digitalWrite(TRIG_PIN, HIGH);
  delayMicroseconds(10);

  digitalWrite(TRIG_PIN, LOW);

  long duration =
      pulseIn(ECHO_PIN, HIGH, 30000);

  if (duration == 0)
    return 999.0;

  return duration * 0.0343 / 2.0;
}

void connectWiFi()
{
  WiFi.begin(WIFI_SSID, WIFI_PASSWORD);

  Serial.print("Connecting");

  while (WiFi.status() != WL_CONNECTED)
  {
    delay(500);
    Serial.print(".");
  }

  Serial.println();
  Serial.println("WiFi connected");
  Serial.println(WiFi.localIP());
}

void localWarning(float acceleration, float distance)
{
  bool dangerous = false;

  if (acceleration > 2.5)
    dangerous = true;

  if (distance < 100)
    dangerous = true;

  if (dangerous)
  {
    digitalWrite(RED_LED, HIGH);
    digitalWrite(GREEN_LED, LOW);
    digitalWrite(BUZZER_PIN, HIGH);
  }
  else
  {
    digitalWrite(RED_LED, LOW);
    digitalWrite(GREEN_LED, HIGH);
    digitalWrite(BUZZER_PIN, LOW);
  }
}

Install these Arduino libraries:



MPU6050
ArduinoJson
WiFi
HTTPClient
Wire

19. Example ESP32 JSON

The ESP32 sends:



{
  "device_id": "ESP32-ACCIDENT-01",
  "timestamp": 123456,
  "imu": {
    "ax": 0.12,
    "ay": -0.08,
    "az": 1.02,
    "gx": 2.1,
    "gy": 1.2,
    "gz": 0.8
  },
  "distance_cm": 75.2,
  "acceleration_g": 1.03
}

20. Jetson accident detection

The Jetson receives:



Camera data
+
ESP32 sensor data

Then performs:



AI Detection
     ↓
Object tracking
     ↓
Obstacle analysis
     ↓
Sensor fusion
     ↓
Risk score

21. Risk-engine Python code

Create:



jetson/risk_engine.py



import math


def clamp(value, minimum=0, maximum=100):
    return max(minimum, min(maximum, value))


def acceleration_risk(acceleration_g):
    """
    Approximate risk based on acceleration magnitude.
    This is a prototype threshold model, not a certified
    automotive safety algorithm.
    """

    if acceleration_g < 1.5:
        return 0

    if acceleration_g < 2.0:
        return 25

    if acceleration_g < 2.5:
        return 50

    if acceleration_g < 3.0:
        return 75

    return 100


def distance_risk(distance_cm):

    if distance_cm > 300:
        return 0

    if distance_cm > 200:
        return 20

    if distance_cm > 120:
        return 40

    if distance_cm > 70:
        return 70

    return 100


def vision_risk(
    object_detected,
    estimated_distance,
    closing_speed
):

    if not object_detected:
        return 0

    risk = 20

    if estimated_distance < 200:
        risk += 20

    if estimated_distance < 100:
        risk += 30

    if estimated_distance < 50:
        risk += 30

    if closing_speed > 10:
        risk += 20

    return clamp(risk)


def calculate_risk(
    acceleration,

22. Jetson camera program

For the first prototype, structure your camera application like this:



import cv2

camera = cv2.VideoCapture(0)

if not camera.isOpened():
    raise RuntimeError("Camera could not be opened")

while True:

    ret, frame = camera.read()

    if not ret:
        break

    # AI inference goes here
    # detections = detector(frame)

    cv2.imshow(
        "Accident Prevention AI",
        frame
    )

    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

camera.release()
cv2.destroyAllWindows()

For the final implementation, replace the comment with your selected YOLO/TensorRT detector.

23. YOLO detection concept

The output can look like:



detections = [
    {
        "class": "car",
        "confidence": 0.91,
        "x1": 200,
        "y1": 150,
        "x2": 500,
        "y2": 400
    },
    {
        "class": "person",
        "confidence": 0.87,
        "x1": 600,
        "y1": 200,
        "x2": 680,
        "y2": 410
    }
]

Then:



Bounding box
      ↓
Object size
      ↓
Approximate distance
      ↓
Object movement
      ↓
Collision probability

24. Time-to-collision concept

If:



D = current distance
V = closing velocity

then:



TTC = D / V

For example:



Distance = 20 m
Closing speed = 10 m/s

TTC = 20 / 10
    = 2 seconds

A low TTC is dangerous.

A simple model:



def calculate_ttc(distance_m, closing_speed_mps):

    if closing_speed_mps <= 0:
        return float("inf")

    return distance_m / closing_speed_mps

Then:



ttc = calculate_ttc(
    distance_m,
    closing_speed
)

if ttc < 1.0:
    risk = 100

elif ttc < 2.0:
    risk = 80

elif ttc < 3.0:
    risk = 60

else:
    risk = 20

This should be presented as a prototype estimation, not a certified automotive collision-warning algorithm.

25. Sensor fusion

The strongest feature of the project is sensor fusion.

Instead of:



Camera → Accident

use:



Camera
   +
Accelerometer
   +
Gyroscope
   +
Distance
   +
GPS
   +
Vehicle speed
   ↓
Sensor Fusion
   ↓
Risk Engine

Example:



Camera:
Car approaching

Distance:
45 cm

Acceleration:
2.7 g

Gyroscope:
abnormal rotation

       ↓

AI Risk Engine

       ↓

Risk = 91%

       ↓

CRITICAL

26. Accident confirmation logic

To prevent false alarms, don't trigger an emergency from one sensor alone.

Use multiple conditions.

For example:



def accident_confirmed(
    acceleration_g,
    distance_cm,
    camera_collision,
    sudden_rotation
):

    impact = acceleration_g > 3.0

    obstacle = distance_cm < 40

    evidence = 0

    if impact:
        evidence += 1

    if obstacle:
        evidence += 1

    if camera_collision:
        evidence += 1

    if sudden_rotation:
        evidence += 1

    return evidence >= 2

This gives a simple explainable multi-sensor confirmation mechanism.

27. n8n architecture

n8n is the central automation/orchestration layer.

The Webhook node receives external HTTP events and can start an n8n workflow. n8n provides separate test and production webhook URLs, which is useful when developing this project.

Create workflow:



             ┌───────────────┐
             │ n8n Webhook   │
             └───────┬───────┘
                     │
                     ▼
             ┌───────────────┐
             │ Validate JSON │
             └───────┬───────┘
                     │
                     ▼
             ┌───────────────┐
             │ Risk Analysis │
             └───────┬───────┘
                     │
                     ▼
             ┌───────────────┐
             │   AI Agent    │
             └───────┬───────┘
                     │
                     ▼
             ┌───────────────┐
             │ IF CRITICAL?  │
             └───┬───────┬───┘
                 │       │
                YES      NO
                 │       │
                 ▼       ▼
            Emergency   Logging
            Workflow    Workflow

28. n8n workflow

Recommended complete workflow:



Webhook
   ↓
Set / Code
   ↓
Validate Device
   ↓
Calculate / Normalize Risk
   ↓
AI Agent
   ↓
Switch Risk Level
   │
   ├── SAFE
   │     ↓
   │  Google Sheets
   │     ↓
   │  ThingSpeak
   │
   ├── WARNING
   │     ↓
   │  Telegram Text
   │     ↓
   │  Google Sheets
   │     ↓
   │  ThingSpeak
   │
   ├── HIGH
   │     ↓
   │  AI-generated message
   │     ↓
   │  Telegram
   │     ↓
   │  Google Sheets
   │     ↓
   │  ThingSpeak
   │
   └── CRITICAL
         ↓
      AI Agent
         ↓
      Generate alert
         ↓
      Text-to-Speech
         ↓
      Telegram Voice
         ↓
      Telegram Text
         ↓
      Google Sheets
         ↓
      ThingSpeak

29. n8n node list

Create these nodes:

Node 1

Webhook

Method:



POST

Path:



accident-event

Authentication:



Header Auth

or JWT/basic authentication.

n8n supports webhook authentication options such as Basic Auth, Header Auth and JWT.

Node 2

Code

Validate the incoming payload.

Example:



const data = $json;

if (!data.device_id) {
  throw new Error("Missing device_id");
}

if (typeof data.risk_score !== "number") {
  throw new Error("Missing risk_score");
}

return [
  {
    json: {
      ...data,
      received_at: new Date().toISOString()
    }
  }
];

30. AI Agent

Configure an n8n AI Agent with instructions such as:



You are an accident-prevention IoT monitoring agent.

Analyze the incoming vehicle safety event.

Input contains:
- device ID
- risk score
- risk level
- acceleration
- distance
- camera detection
- GPS location
- timestamp

Your responsibilities:

1. Interpret the event.
2. Determine whether it is SAFE, WARNING, HIGH or CRITICAL.
3. Explain the reason briefly.
4. Recommend the appropriate action.
5. If CRITICAL, create a concise emergency alert.
6. Never invent sensor measurements.
7. Never claim that an accident is confirmed unless the
   supplied evidence supports it.

Return JSON:

{
  "severity": "...",
  "summary": "...",
  "recommended_action": "...",
  "telegram_message": "...",
  "voice_message": "..."
}

This makes the AI agent decision-support software, while the deterministic risk engine remains responsible for the primary thresholding.

That separation is important:



Safety logic
     ↓
Deterministic rules

AI Agent
     ↓
Interpretation + explanation + orchestration

rather than allowing an LLM to arbitrarily control safety-critical decisions.

31. Example AI Agent input



{
  "device_id": "VEHICLE-001",
  "risk_score": 89,
  "risk_level": "CRITICAL",
  "acceleration_g": 3.6,
  "distance_cm": 35,
  "camera_collision": true,
  "latitude": 16.50,
  "longitude": 80.64
}

32. Example AI Agent output



{
  "severity": "CRITICAL",
  "summary": "Possible collision detected from combined camera, acceleration and distance evidence.",
  "recommended_action": "Stop the vehicle if safe and verify the occupants.",
  "telegram_message": "🚨 CRITICAL: Possible collision detected. Risk 89%. Acceleration 3.6g and obstacle distance 35 cm.",
  "voice_message": "Critical safety alert. A possible collision has been detected. Please check the vehicle and occupants immediately."
}

33. Telegram text alert

Telegram's Bot API provides sendVoice for sending playable voice messages; Telegram documents support for OGG/Opus, MP3 and M4A voice uploads, with the current documented size limit of 50 MB.

Example text:



🚨 ACCIDENT PREVENTION ALERT

Vehicle: VEHICLE-001

Risk: 89%
Status: CRITICAL

Acceleration: 3.6 g
Obstacle: 35 cm
Camera: Possible collision

Recommended action:
Stop safely and check the occupants.

Location:
16.5000, 80.6400

34. Telegram voice workflow

The voice pipeline is:



AI Agent
   ↓
voice_message
   ↓
Text-to-Speech API
   ↓
MP3/M4A/OGG
   ↓
n8n binary data
   ↓
Telegram Bot API
   ↓
User receives voice message

You can use an external TTS provider or your own local TTS service.

35. Telegram Bot setup

In Telegram:



BotFather
   ↓
/newbot
   ↓
Create bot
   ↓
Copy Bot Token

Then send a message to your bot.

Obtain your chat ID using the Telegram Bot API.

Store the bot token in n8n credentials rather than putting it directly into source code. n8n's Telegram credential uses a bot API access token.

36. Google Sheets database

Create:



Accident_Log

Columns:



Timestamp
Device_ID
Risk_Score
Risk_Level
Acceleration
Distance
Camera_Status
Latitude
Longitude
AI_Summary
Recommended_Action
Telegram_Status
Voice_Status

Example:

Timestamp	Device	Risk	Level	Accel	Distance	AI Summary
16:05	VEH001	89	CRITICAL	3.6g	35cm	Possible collision
16:06	VEH001	42	WARNING	1.8g	90cm	Unsafe distance
16:07	VEH001	10	SAFE	1.1g	250cm	Normal

n8n's Google Sheets node supports operations including appending and updating spreadsheet rows.

37. ThingSpeak configuration

Create a channel:

AI Accident Prevention System

Use fields:



Field 1 = Risk Score
Field 2 = Acceleration
Field 3 = Distance
Field 4 = Speed
Field 5 = Vision Risk
Field 6 = Accident Status
Field 7 = Latitude
Field 8 = Longitude

ThingSpeak channels can have up to eight fields.

38. ThingSpeak HTTP request

The documented ThingSpeak write endpoint is:



https://api.thingspeak.com/update.json

with a channel Write API Key and fields in the request.

n8n HTTP Request:



Method:
POST

URL:
https://api.thingspeak.com/update.json

Body:



{
  "api_key": "YOUR_WRITE_API_KEY",
  "field1": "89",
  "field2": "3.6",
  "field3": "35",
  "field4": "42",
  "field5": "90",
  "field6": "CRITICAL",
  "field7": "16.5000",
  "field8": "80.6400"
}

The Write API Key should be kept secret; ThingSpeak documents separate read/write permissions and channel API keys.

39. Web dashboard

The dashboard can show:



┌──────────────────────────────────────────┐
│       AI ACCIDENT PREVENTION SYSTEM      │
├──────────────────────────────────────────┤
│                                          │
│  STATUS          CRITICAL                │
│                                          │
│  RISK SCORE      89%                     │
│                                          │
│  ACCELERATION    3.6 g                   │
│                                          │
│  DISTANCE        35 cm                   │
│                                          │
│  CAMERA          COLLISION SUSPECTED     │
│                                          │
│  LOCATION        16.5000, 80.6400        │
│                                          │
├──────────────────────────────────────────┤
│       LIVE SENSOR GRAPH                  │
│                                          │
│     /\                                    │
│    /  \       /\                          │
│___/    \_____/  \________                 │
│                                          │
└──────────────────────────────────────────┘

40. HTML dashboard

Create:



dashboard/index.html



<!DOCTYPE html>
<html>
<head>
    <meta charset="UTF-8">

    <title>
        AI Accident Prevention Dashboard
    </title>

    <style>

        body {
            margin: 0;
            font-family: Arial, sans-serif;
            background: #101820;
            color: white;
        }

        header {
            padding: 20px;
            background: #17232c;
            text-align: center;
        }

        .container {
            display: grid;
            grid-template-columns:
                repeat(auto-fit, minmax(220px, 1fr));
            gap: 20px;
            padding: 30px;
        }

        .card {
            background: #1c2b36;
            padding: 25px;
            border-radius: 15px;
            box-shadow:
                0 5px 15px rgba(0,0,0,0.3);
        }

        .value {
            font-size: 35px;
            font-weight: bold;
        }

        .critical {
            color: #ff3333;
        }

        .safe {
            color: #00ff88;
        }

        .warning {
            color: #ffc107;
        }

    </style>
</head>

<body>

<header>

<h1>
AI Accident Prevention Dashboard
</h1>

<p>
Jetson Nano + ESP32 + n8n
</p>

</header>

<div class="container">

41. Flask dashboard server

Create:



jetson/server.py



from flask import Flask, jsonify, send_from_directory

app = Flask(__name__)

latest_data = {
    "risk_score": 0,
    "risk_level": "SAFE",
    "acceleration": 0,
    "distance": 999
}


@app.route("/")
def index():

    return send_from_directory(
        "../dashboard",
        "index.html"
    )


@app.route("/api/status")
def status():

    return jsonify(latest_data)


@app.route("/api/update", methods=["POST"])
def update():

    from flask import request

    data = request.get_json()

    latest_data.update(data)

    return jsonify({
        "success": True
    })


if __name__ == "__main__":

    app.run(
        host="0.0.0.0",
        port=5000,
        debug=False
    )

Open:



http://JETSON-IP:5000

42. Complete end-to-end flow

This is the most important flow diagram for your project report.



                    START
                      │
                      ▼
              Initialize System
                      │
          ┌───────────┴───────────┐
          │                       │
          ▼                       ▼
       ESP32                 Jetson Nano
          │                       │
          ▼                       ▼
     Read Sensors             Read Camera
          │                       │
          ▼                       ▼
    MPU6050/Distance          YOLO AI
          │                       │
          └──────────┬────────────┘
                     │
                     ▼
                Data Fusion
                     │
                     ▼
                Risk Engine
                     │
             ┌───────┴────────┐
             │                │
             ▼                ▼
           SAFE             DANGER
             │                │
             │                ▼
             │          Local Warning
             │                │
             │                ▼
             │           n8n Webhook
             │                │
             │                ▼
             │             AI Agent
             │                │
             │       ┌────────┼────────┐
             │       │        │        │
             │       ▼        ▼        ▼
             │   Telegram  Sheets  ThingSpeak
             │       │
             │       ▼
             │   Voice Alert
             │
             ▼
          Dashboard

43. Critical-event flow



Collision suspected
        ↓
Acceleration > threshold?
        ↓
Distance dangerous?
        ↓
Camera collision detected?
        ↓
Evidence count ≥ 2?
        ↓
YES
        ↓
CRITICAL EVENT
        ↓
Send JSON
        ↓
n8n
        ↓
AI Agent
        ↓
Generate emergency message
        ↓
Generate voice text
        ↓
TTS
        ↓
Telegram Voice
        +
Telegram Text
        +
Google Sheets
        +
ThingSpeak
        +
Web Dashboard

44. Example n8n data



{
  "device_id": "VEHICLE-001",
  "timestamp": "2026-08-16T16:10:00+05:30",

  "risk_score": 91,

  "risk_level": "CRITICAL",

  "sensors": {
    "acceleration_g": 3.7,
    "distance_cm": 31,
    "gyro_x": 48.2,
    "gyro_y": 12.4,
    "gyro_z": 35.1
  },

  "vision": {
    "vehicle_detected": true,
    "collision_probability": 0.91,
    "estimated_distance_m": 0.31
  },

  "gps": {
    "latitude": 16.5000,
    "longitude": 80.6400
  }
}

45. n8n decision tree



                 Risk Score
                     │
          ┌──────────┼──────────┐
          │          │          │
        <30       30-49       50-74       ≥75
          │          │          │           │
        SAFE      WARNING      HIGH      CRITICAL
          │          │          │           │
       Log only   Telegram   Telegram    Telegram
                    │          │           │
                    │          │        Voice Alert
                    │          │           │
                    └──────────┴───────────┤
                                           │
                                      Google Sheets
                                           │
                                        ThingSpeak

46. Agentic IoT concept

The phrase Agentic IoT in your project can be explained as:

An IoT system in which sensor events are not merely transmitted to a dashboard, but are interpreted by an AI agent that selects and coordinates subsequent digital actions.

Traditional IoT:



Sensor
  ↓
Cloud
  ↓
Dashboard

Your proposed Agentic IoT:



Sensor
  ↓
Edge AI
  ↓
Event
  ↓
AI Agent
  ↓
Reason
  ↓
Choose action
  ├── Log
  ├── Notify
  ├── Voice alert
  ├── Update cloud
  └── Escalate

47. Why n8n is important

Without n8n:



Jetson → Telegram
Jetson → Sheets
Jetson → ThingSpeak
Jetson → TTS

The Jetson would need to implement all integrations.

With n8n:



Jetson
   ↓
ONE WEBHOOK
   ↓
n8n
 ┌─┼───────────────┐
 ↓ ↓               ↓
AI Telegram     Google
   ↓              Sheets
Voice
   ↓
ThingSpeak

This makes the system modular.

n8n's Webhook node is specifically intended to receive events from external applications and start workflows.

48. Telegram alert levels

SAFE

No Telegram alert.

WARNING



⚠️ WARNING

Vehicle approaching an obstacle.

Risk: 38%
Distance: 95 cm

Please reduce speed.

HIGH



⚠️ HIGH RISK

Unsafe driving condition detected.

Risk: 67%
Distance: 54 cm
Acceleration: 2.4 g

Immediate attention recommended.

CRITICAL



🚨 CRITICAL ACCIDENT ALERT

Possible collision detected.

Risk: 91%

Acceleration: 3.7 g
Obstacle distance: 31 cm
Vision collision probability: 91%

Please check the vehicle and occupants immediately.

49. Voice alert

Example:

"Critical safety alert. A possible collision has been detected. Please stop safely and check the vehicle and occupants immediately."

This is preferable to sending a long paragraph as voice.

50. Google Sheets flow



Event
 ↓
n8n
 ↓
AI Agent
 ↓
Prepare Row
 ↓
Google Sheets
 ↓
Append Row

Example row:



2026-08-16 16:10
VEHICLE-001
91
CRITICAL
3.7
31
0.91
16.5000
80.6400
Possible collision
Check occupants
SENT
SENT

51. ThingSpeak flow



ESP32/Jetson
      ↓
n8n
      ↓
HTTP Request
      ↓
ThingSpeak REST API
      ↓
Channel
      ↓
Graph

ThingSpeak supports HTTP REST calls for updating channel data, as well as MQTT for IoT applications.

52. Dashboard architecture



              ThingSpeak
                  │
                  │
            Cloud Graphs
                  │
                  ▼
              Web User


Jetson
  │
  ▼
Flask Server
  │
  ▼
HTML/CSS/JS
  │
  ▼
Live Dashboard

You can therefore have two dashboard levels:

Local dashboard

Hosted by Jetson.

Cloud dashboard

ThingSpeak.

53. Communication protocols

Connection	Protocol
ESP32 → n8n	HTTP POST
ESP32 → Jetson	HTTP/MQTT optional
Camera → Jetson	USB/CSI
Jetson → n8n	HTTP POST
n8n → Telegram	Bot API
n8n → Sheets	Google API
n8n → ThingSpeak	REST API
Dashboard → Jetson	HTTP
ESP32 → Wi-Fi	TCP/IP

54. Recommended security

Do not expose an unauthenticated webhook to the public Internet.

Use:



HTTPS
+
Header authentication
+
Random webhook path
+
Secret API key

For example:



X-DEVICE-TOKEN: YOUR_SECRET_TOKEN

n8n supports authentication mechanisms on Webhooks, including header authentication.

55. Do not put secrets in source code

Bad:



const char* TELEGRAM_TOKEN =
    "123456:ABCDEF...";

Better:



ESP32
   ↓
n8n authenticated webhook
   ↓
n8n credentials
   ├── Telegram token
   ├── Google credentials
   ├── AI credentials
   ├── TTS credentials
   └── ThingSpeak key

This architecture keeps most cloud secrets out of the ESP32.

56. Error-handling architecture

Your system should continue working if one cloud service fails.



                Critical Event
                     │
                     ▼
                   n8n
                     │
         ┌───────────┼───────────┐
         │           │           │
      Telegram    Sheets     ThingSpeak
         │           │           │
       FAIL         OK          OK
         │
         ▼
   Retry / fallback
         │
         ▼
   Local Jetson log

Always retain a local emergency event record.

57. Offline operation

This is a major feature you can mention in the project report.

If Internet fails:



Camera
 ↓
Jetson AI
 ↓
Risk Engine
 ↓
LOCAL BUZZER
 ↓
LOCAL LOG

When Internet returns:



Local events
     ↓
Upload queue
     ↓
n8n
     ↓
Cloud services

Therefore accident prevention doesn't completely depend on cloud connectivity.

58. Local fail-safe

For a critical system:



Cloud AI unavailable
        ↓
Use deterministic local rules
        ↓
Continue warning

Do not design the vehicle safety function so that:



Internet failure = no safety

59. State machine

The entire system can be represented as:



             ┌──────────┐
             │   SAFE   │
             └────┬─────┘
                  │
             risk > 30
                  │
                  ▼
             ┌──────────┐
             │ WARNING  │
             └────┬─────┘
                  │
             risk > 50
                  │
                  ▼
             ┌──────────┐
             │   HIGH   │
             └────┬─────┘
                  │
             risk > 75
                  │
                  ▼
             ┌──────────┐
             │ CRITICAL │
             └────┬─────┘
                  │
             event resolved
                  │
                  ▼
                SAFE

60. Project sequence

Build it in stages.

Stage 1 — ESP32

First make:



ESP32
 ↓
MPU6050
 ↓
Serial Monitor

Verify acceleration.

Then:



ESP32
 ↓
Ultrasonic

Then:



ESP32
 ↓
Wi-Fi
 ↓
HTTP

Stage 2 — n8n

Create:



Webhook
 ↓
Respond

Send test request:



curl -X POST \
https://YOUR-N8N/webhook/accident-sensor \
-H "Content-Type: application/json" \
-d '{
  "device_id":"TEST",
  "risk_score":80,
  "risk_level":"CRITICAL"
}'

n8n documents using HTTP requests/curl to trigger Webhooks during testing.

61. Stage 3 — Telegram

Test:



Webhook
 ↓
Telegram

Expected:



🚨 Test accident alert

Only after this works should you add AI.

62. Stage 4 — Google Sheets

Build:



Webhook
 ↓
Google Sheets

Check that every event creates a row.

63. Stage 5 — ThingSpeak

Build:



Webhook
 ↓
HTTP Request
 ↓
ThingSpeak

Check the channel graph.

64. Stage 6 — AI Agent

Then:



Webhook
 ↓
AI Agent
 ↓
Telegram

Test several cases:



Risk = 10
Risk = 40
Risk = 60
Risk = 90

65. Stage 7 — Jetson

Connect camera.

Run:



Camera
 ↓
Object detector
 ↓
Display bounding boxes

Do not immediately connect emergency alerts.

First verify detection.

66. Stage 8 — Sensor fusion

Finally:



Jetson camera
+
ESP32
 ↓
Risk engine
 ↓
n8n

67. Testing cases

Your project report should include a test table.

Test	Camera	Accel	Distance	Expected
Normal	Clear	1.0g	300cm	SAFE
Obstacle	Vehicle	1.2g	100cm	WARNING
Rapid approach	Vehicle	1.8g	70cm	HIGH
Hard impact	Collision	3.5g	30cm	CRITICAL
Camera false positive	Object	1.0g	300cm	SAFE
Sensor false positive	Clear	3.5g	300cm	Verify
Network failure	Critical	3.5g	30cm	Local alert

68. Performance metrics

Measure:

Detection accuracy



Accuracy =
Correct detections /
Total detections

Precision



Precision =
TP / (TP + FP)

Recall



Recall =
TP / (TP + FN)

F1 score



F1 =
2 × Precision × Recall /
(Precision + Recall)

Alert latency

Measure:



Sensor event
     ↓
Detection timestamp
     ↓
n8n received
     ↓
Telegram sent

Then:



Latency =
Telegram timestamp -
Sensor event timestamp

69. Suggested performance table

Fill this using your actual experimental results:

Parameter	Target
Camera FPS	10–20 FPS
Sensor interval	100–500 ms
Cloud telemetry	5–15 sec
Critical alert	<5 sec target
False alarm rate	Minimize
Object detection	>80% test accuracy
Dashboard update	<5–10 sec

Do not claim these numbers as achieved until you measure them.

70. Advantages

1. Edge AI

Computer vision runs locally.

2. Sensor fusion

Combines camera and physical sensors.

3. Early warning

Attempts to detect risk before an accident.

4. Cloud logging

Events are permanently recorded.

5. AI explanation

The AI agent converts raw sensor data into human-readable reasoning.

6. Telegram voice

Users can receive an audible emergency notification.

7. Automation

n8n connects all services.

8. Modular architecture

Individual components can be replaced.

71. Limitations

This is extremely important in your documentation.

The system is a prototype/research/educational safety system, not a certified automotive safety product.

Limitations include:

Camera lighting affects detection.
Rain/fog can reduce vision accuracy.
Object detection can generate false positives.
Ultrasonic sensors have limited range.
GPS may be unavailable indoors.
ESP32 Wi-Fi depends on network availability.
AI-agent output should not directly control safety-critical systems.
Risk thresholds require calibration.
A single camera cannot reliably determine real-world distance in all conditions.
Jetson Nano has limited computational resources.
JetPack 4 is end-of-life for the Nano.

72. Important safety architecture

For your final project, I strongly recommend this separation:



                  AI Agent
                     │
              Explanation only
                     │
                     ▼
                Notification

while:



Camera
 +
Sensors
 ↓
Deterministic Risk Engine
 ↓
Local Safety Warning

In other words:

Never let an LLM be the sole authority for emergency braking or another safety-critical physical action.

The AI Agent should interpret, summarize, log and notify.

73. Complete project workflow



START
  │
  ▼
Power ON
  │
  ├─────────────┐
  │             │
  ▼             ▼
ESP32          Jetson
  │             │
  ▼             ▼
Sensors       Camera
  │             │
  ▼             ▼
Sensor JSON   YOLO
  │             │
  └──────┬──────┘
         ▼
    Sensor Fusion
         │
         ▼
    Risk Calculation
         │
         ▼
 ┌───────┼─────────┐
 │       │         │
 ▼       ▼         ▼
SAFE  WARNING   CRITICAL
 │       │         │
 │       ▼         ▼
 │    Local      Local
 │    warning    warning
 │       │         │
 └───────┴────┬────┘
              ▼
         n8n Webhook
              │
              ▼
           AI Agent
              │
       ┌──────┼────────┐
       ▼      ▼        ▼
    Telegram Sheets ThingSpeak
       │
       ▼
   Voice Alert
       │
       ▼
      User

74. Recommended final hardware architecture

For the demonstration model:



              CAMERA
                 │
                 ▼
       ┌─────────────────┐
       │  NVIDIA JETSON  │
       │      NANO       │
       │                 │
       │ YOLO + OpenCV   │
       │ Risk Engine     │
       │ Flask Dashboard │
       └────────┬────────┘
                │
             Wi-Fi
                │
                ▼
       ┌─────────────────┐
       │      n8n        │
       │                 │
       │ Webhook         │
       │ AI Agent        │
       │ Automation      │
       └───┬────┬────┬───┘
           │    │    │
           ▼    ▼    ▼
       Telegram Sheets ThingSpeak
           │
           ▼
        Voice Alert


        ESP32 SENSOR UNIT

       ┌─────────────────┐
       │      ESP32      │
       ├─────────────────┤
       │ MPU6050         │
       │ Ultrasonic      │
       │ GPS             │
       │ Buzzer          │
       │ LEDs            │
       └─────────────────┘

75. Suggested project presentation

For your viva/demo, demonstrate in this order:

Demo 1

Normal condition:



Risk = 10%
Status = SAFE

Demo 2

Place obstacle close to ultrasonic sensor:



Distance = 50 cm
Risk = WARNING
Buzzer = ON

Demo 3

Simulate sudden movement using MPU6050:



Acceleration = 3g+
Risk = HIGH/CRITICAL

Demo 4

Camera sees obstacle/vehicle:



YOLO → Object detected

Demo 5

Combine the conditions:



Object detected
+
Low distance
+
High acceleration

Then:



CRITICAL
 ↓
n8n
 ↓
AI Agent
 ↓
Telegram
 ↓
Voice alert
 ↓
Google Sheets
 ↓
ThingSpeak

This makes the complete system visually impressive.

76. What to show in the final demonstration

Have these five screens open simultaneously:

Screen 1

Jetson camera:



[Camera]
Car 91%
Person 87%
Distance warning

Screen 2

Terminal:



Risk Score: 89
Risk Level: CRITICAL

Screen 3

n8n:



Webhook
 → AI Agent
 → Telegram
 → Google Sheets
 → ThingSpeak

Screen 4

Google Sheets:



Timestamp | Risk | Acceleration | Distance

Screen 5

Telegram:



🚨 CRITICAL ALERT
Possible collision detected...

and:



🔊 Voice message

77. Project modules for your report

You can divide the project into these modules:

Module 1 — ESP32 IoT Sensor Module

Collects:



Acceleration
Gyroscope
Distance
GPS

Module 2 — Edge AI Module

Runs:



Camera
YOLO
Object detection
Tracking

Module 3 — Risk Analysis Module

Performs:



Sensor fusion
Risk scoring
Accident confirmation

Module 4 — IoT Communication Module

Uses:



HTTP
Wi-Fi
JSON
REST

Module 5 — Agentic AI Module

Uses:



n8n
AI Agent
Reasoning
Action selection

Module 6 — Notification Module

Uses:



Telegram
Text
Voice

Module 7 — Cloud Logging

Uses:



Google Sheets
ThingSpeak

Module 8 — Web Dashboard

Uses:



HTML
CSS
JavaScript
Flask

78. Expected output

The final system should produce:



LIVE CAMERA
      +
LIVE SENSOR DATA
      +
AI DETECTION
      +
RISK SCORE
      ↓
ACCIDENT PREVENTION
      ↓
AUTOMATED RESPONSE

Example final output:



=========================================
 AI ACCIDENT PREVENTION SYSTEM
=========================================

Device       : VEHICLE-001
Status       : CRITICAL

Risk Score   : 91%

Acceleration : 3.7 g
Distance     : 31 cm

Camera       : Collision suspected
GPS          : 16.5000, 80.6400

AI Analysis:
Possible collision detected from
combined sensor and vision evidence.

Action:
Check vehicle and occupants.

Telegram     : SENT
Voice Alert  : SENT
Google Sheet : LOGGED
ThingSpeak   : UPDATED

=========================================

79. Future enhancements

For a stronger version 2:

Computer vision

YOLOv8/YOLO11 or a Nano-compatible optimized detector
DeepSORT/ByteTrack
Monocular depth estimation
Lane detection
Driver drowsiness detection
Traffic-sign detection

Sensors

mmWave radar
LiDAR
wheel-speed sensor
steering angle
CAN bus

AI

Temporal accident classifier
LSTM
Transformer-based video model
Predictive risk model
RAG-based incident assistant

IoT

MQTT
AWS/Azure
Firebase
InfluxDB
Grafana

Communication

GSM
LTE
NB-IoT
LoRaWAN

Emergency system



Accident
   ↓
GPS
   ↓
Telegram
   ↓
Emergency contact
   ↓
Hospital/police integration

80. Recommended project name for your certificate/report

“Design and Implementation of an AI-Based Accident Prevention and Agentic IoT Alert System Using NVIDIA Jetson Nano and ESP32”

Alternative:

“AI-Powered Accident Detection and Prevention Using Edge Computer Vision, ESP32 IoT and n8n Agentic Automation”

Or a shorter title:

“Agentic AI-Based Accident Prevention and IoT Alert System”

81. Official documentation/resources

For the Jetson setup, use NVIDIA's official Jetson Nano documentation and JetPack archive. NVIDIA's current documentation identifies JetPack 4.6.6 as the final JetPack 4 release supporting Nano.

NVIDIA Jetson Nano Developer Documentation

NVIDIA JetPack Archive

For n8n:

n8n Webhook documentation

n8n Google Sheets documentation

n8n Telegram documentation

For ThingSpeak:

ThingSpeak REST API documentation

ThingSpeak Write Data documentation

For Telegram voice:

Telegram Bot API

Final architecture in one line



ESP32 Sensors + Camera
        ↓
NVIDIA Jetson Nano Edge AI
        ↓
Sensor Fusion + Accident Risk Engine
        ↓
Secure n8n Webhook
        ↓
AI Agent
        ↓
Telegram Text + TTS Voice Alert
        +
Google Sheets Logging
        +
ThingSpeak Cloud Dashboard
        +
Jetson Web Dashboard

This gives you a complete, defensible final-year project architecture: the Jetson performs edge AI, ESP32 provides physical IoT sensing, deterministic logic performs safety/risk decisions, and the n8n AI Agent handles interpretation and multi-service automation. It also avoids making an LLM the sole controller of a safety-critical function.

Sunday, 16 August 2026

AI Based Accident Prevention System using NVIDIA Jetson Nano

AI-Based Accident Prevention and Intelligent IoT Alert System Using NVIDIA Jetson Nano, ESP32, AI Agent, n8n, Telegram, Google Sheets and ThingSpeak

1. Project title

Short title

Technologies

2. Abstract

3. Main objective

4. Secondary objectives

5. Proposed system architecture

High-level architecture

6. Detailed block diagram

7. Hardware requirements

Required hardware

8. Optional hardware

9. ESP32 sensor subsystem

MPU6050

Ultrasonic

GPS

Local warning

10. ESP32 schematic

Important HC-SR04 note

11. Recommended ESP32 pin table

12. Accident prevention concept

Level 0 — SAFE

Level 1 — WARNING

Level 2 — HIGH RISK

Level 3 — CRITICAL

13. Risk calculation

14. Computer vision subsystem

15. Why Jetson Nano?

16. Jetson software installation

17. Project directory

18. ESP32 software

19. Example ESP32 JSON

20. Jetson accident detection

21. Risk-engine Python code

22. Jetson camera program

23. YOLO detection concept

24. Time-to-collision concept

25. Sensor fusion

26. Accident confirmation logic

27. n8n architecture

28. n8n workflow

29. n8n node list

Node 1

Node 2

30. AI Agent

31. Example AI Agent input

32. Example AI Agent output

33. Telegram text alert

34. Telegram voice workflow

35. Telegram Bot setup

36. Google Sheets database

37. ThingSpeak configuration

38. ThingSpeak HTTP request

39. Web dashboard

40. HTML dashboard

41. Flask dashboard server

42. Complete end-to-end flow

43. Critical-event flow

44. Example n8n data

45. n8n decision tree

46. Agentic IoT concept

47. Why n8n is important

48. Telegram alert levels

SAFE

WARNING

HIGH

CRITICAL

49. Voice alert

50. Google Sheets flow

51. ThingSpeak flow

52. Dashboard architecture

Local dashboard

Cloud dashboard

53. Communication protocols

54. Recommended security

55. Do not put secrets in source code

56. Error-handling architecture