Neurora Behavioral Intelligence{Case Study}

A Platform for Driver Attention Stress and Safety

Fusing synchronized driving video, eye-gaze and heart-rate data into automated behavioral scores — showing researchers, fleets and driving schools not just what a driver did, but where they looked, what they missed and when stress took over.

Platform:

CASE STUDY

01 — ABOUT THE CLIENT

Where driver safety is still judged by eye

Neurora Labs is building a Behavioral Intelligence Platform for organizations that need to understand how people actually drive. Its users are driving schools, fleet safety and risk teams, academic and commercial driving-behavior researchers, and automotive safety engineers. Each of them depends on evidence from real driving sessions—video, gaze, heart rate—and each has been forced to rely on an instructor's memory and judgment to interpret it.

02 — THE CHALLENGE

Four gaps between the drive and the insight

Understanding real driver behavior is difficult — attention, stress, and hazard response happen in an instant and leave no objective trace. Building a reliable behavioral intelligence system required fusing video, gaze, and physiological data into one accurate, real-time picture.

Manual, after-the-fact review

Instructors scored sessions from observation and post-drive recall, so feedback arrived late, and its quality varied with whoever happened to be watching.

No objective attention data

Nothing recorded where a driver actually looked or what they missed. Hazard awareness was inferred from behavior, never measured directly.

Sensors that never talked

Existing tools captured video without gaze, or tracked eyes without driving context. No product fused video, gaze, and physiology on one timeline.

Subjective, inconsistent scoring

Safety judgements rested on individual opinion, making results hard to compare across drivers, sessions, or organizations, and impossible to benchmark at scale.

03 — THE SOLUTION

One capture-to-insight pipeline, three connected surfaces

Soft Suave is delivering an end-to-end system for Neurora Labs: Neurora Capture records synchronized video, gaze, and heart-rate data; an AWS backend runs SQS-triggered ECS workers for computer vision and scoring; and Neurora Insight, the Neurora Self View driver app, and an admin panel turn the output into decisions.

Neurora Capture

A mobile recording app that captures camera video, eye-gaze coordinates, and pupil size from wearable tracking glasses, along with heart-rate signals from a wearable sensor—all timestamped and synchronized on-device.

AI Processing Pipeline

Uploaded sessions trigger cloud workers that detect and track objects across frames, map gaze to dynamic Areas of Interest, and compute fixation duration, PERCLOS, time-to-first-fixation, and gaze entropy.

Insight, Self View & Admin

A React dashboard replays the drive with gaze overlays, heatmaps, scan paths, and bounding boxes alongside safety, focus, stress, and risk scores—plus the Self View driver app and an admin panel for user and session control.

"Raw sensor streams in, behavioral scores and plain-language safety insight out — automated end to end, with human-adjustable Areas of Interest."

04 — KEY FEATURES

Twelve capabilities, one platform

The platform integrates synchronized multi-sensor capture, AI-driven gaze mapping, and automated behavioral scoring into a unified system. These capabilities enable objective attention tracking, stress detection, and plain-language safety insight across every driving session.

Synchronized Video, Gaze & Heart-Rate Capture
AI Object Detection & Frame Tracking
Dynamic Area-of-Interest Gaze Mapping
TFixation, PERCLOS, TTFF & Gaze Entropy
Automated Safety, Focus & Risk Scoring
Stress & Cognitive Load Modeling

Attention Heatmaps & Scan-Path Replay
AI-Generated Plain-Language Session Insights
Hybrid Manual AOI Adjustment
Neurora Self View Driver App
Report Export & Session Sharing
Secure AWS Storage & Admin Panel

Cloud-based Data and Backup Operations Platform interface

05 — RESULTS & IMPACT

A delivery plan built to de-risk the platform

Delivered through a milestone-driven, PoC-first approach, the platform de-risked development while accelerating time-to-market — turning raw sensor data into a production-ready behavioral intelligence system in just 5 months.

5 months

End-to-end delivery from kick-off to complete application, across a milestone-driven release plan

2 Stream

AI and application development run in parallel—12 weeks and 10 weeks—to cut time-to-market

2–3 Weeks

Milestone 0 proof of concept, delivered at no cost, with Soft Suave bearing PoC hosting costs

90 Days

Post-deployment support by the original project team after go-live

06 — WHY SOFT SUAVE

Multi-sensor AI, delivered on a proven cadence

This is not a dashboard project—it is sensor fusion, computer vision, gaze mapping, and behavioral modeling delivered as one production platform. Soft Suave brings 13+ years of engineering depth and a milestone-driven agile cadence: a no-cost proof of concept first, followed by parallel AI and application streams tracked in Jira and Confluence with full client visibility. Every milestone ships to a client-owned repository, backed by layered functional, integration, and regression testing, along with a 90-day post-deployment support period.

Download this practical case study to learn how our Behavioral Intelligence platform fuses video, gaze, and heart-rate data into automated driver safety and stress scores.