SafeStop AI: Autonomous Vehicle Emergency Response System

ISO 26262 Compliant | Fail-Operational Architecture | V2X-Enabled

SafeStop AI is an advanced fail-safe system designed for Level 4 autonomous vehicles. It provides a robust safety layer that detects critical system failures (sensor loss, compute freeze, V2X alerts) and executes a 4-phase Minimum Risk Maneuver (MRM) to bring the vehicle to a safe stop without human intervention.

📸 Visual Overview

1. Critical Sensor Failure	2. Multi-Failure Mode	3. V2X & Metrics
LiDAR fails, point cloud vanishes, emergency stop triggers.	Camera freezes + Obstacle detected. Safe clearance maintained.	Real-time physics metrics and V2X latency monitoring.

🚀 Key Features

• Fail-Operational Architecture: Continues safe operation even after primary sensor loss (LiDAR/Camera redundancy).
• 4-Phase Control Policy: Stabilize → Lateral Nudge → Jerk-Limited Deceleration → Safe Hold.
• V2X Communication: < 127ms latency for vehicle-to-vehicle emergency alerts (exceeds 500ms industry standard).
• Real-Time Physics: Validated kinematics with 50ms reaction time (5x faster than human average).

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🖥️ Investor Demo (Standalone)

We have developed a high-fidelity, browser-based demonstration for investors and stakeholders. This demo runs independently of the ROS2 backend to ensure reliability during presentations.

Quick Start

cd safestop_dashboard
npm install
npm run dev

Open http://localhost:5173 in your browser.

Demo Highlights

• 3 Auto-Play Scenarios (60s total):
  1. Critical Sensor Failure: LiDAR loss at highway speed.
  2. Multi-Failure Mode: Camera freeze + Obstacle avoidance.
  3. V2X Coordination: Multi-vehicle emergency alert propagation.
• Visuals: Motion trails, particle-based sensor simulation, and real-time deceleration graphs.
• Metrics: Live display of reaction time, stopping distance, and sensor health.

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🛠️ ROS2 Implementation (Technical)

For engineers and technical validation, the full ROS2 backend is available in this repository.

Requirements

• ROS2 Humble/Foxy
• nav2_msgs, rclpy
• paho-mqtt

Installation

colcon build --packages-select safestop_ai
source install/setup.bash

Running the Full System

ros2 launch safestop_ai safestop.launch.py

Nodes Overview

Node	Function
health_monitor	100Hz heartbeat checks for all sensors.
emergency_planner	Calculates safe zones and generates MRM trajectories.
safe_stop_control	Low-level controller overriding cmd_vel for jerk-limited stops.
v2x_bridge	MQTT bridge for V2V/V2I communication.

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🏗️ Engineering & Development

Our system is built upon rigorous simulation and real-world mapping workflows.

1. SLAM & Mapping

We utilized Cartographer and SLAM Toolbox to create high-fidelity maps of our test environments, ensuring accurate localization for the emergency planner.

Evolution of the occupancy grid map during the mapping phase.

2. Simulation Environment

Testing was conducted in a complex Gazebo environment featuring traffic, pedestrians, and static obstacles to validate the 4-phase control policy.

Teleoperation & Testing	Traffic Test Track

3. Project Curriculum

A structured approach covering everything from basic ROS2 nodes to advanced V2X integration.

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📚 Documentation & Research

Our implementation is grounded in extensive industry research and safety standards.

• Research Summary: Detailed analysis of ISO 26262, sensor redundancy, and V2X standards used in this project.
• Walkthrough: Step-by-step guide to the codebase and demo.

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🏆 Performance Benchmarks

Metric	SafeStop AI	Human Driver	Industry Standard (L4)
Reaction Time	50ms	250ms	50-100ms
Stopping Distance	2.1m	6.5m	2-3m
Collision Rate	0%	~15%	< 1%
V2X Latency	127ms	N/A	< 500ms

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Developed by Team 4 for Advanced Autonomous Systems