AI-ML based Intelligent Dead Reckoning system for seamless navigation
“When vehicles enter tunnels, underground parking, or dense urban spaces, GPS signals drop completely, causing navigation apps to freeze or drift. This software turns any standard smartphone into a smart navigator that uses built-in motion sensors and AI to track precise vehicle movement without GPS or car cables.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
A dual-deliverable solution consisting of an edge-deployable software engine and a cross-platform mobile application that provides seamless dead reckoning and AI-driven GNSS+INS sensor fusion.
WHO IS IT FOR?
Primary beneficiaries include logistics fleets, quick-commerce riders, ride-hailing drivers, emergency responders, and everyday citizens navigating urban canyons and tunnels. The stakeholder is ISRO / Department of Space.
WHY DOES IT EXIST?
Consumer smartphones rely on GPS which fails in obstructed environments due to signal attenuation and multipath effects. Low-cost smartphone sensors suffer from high noise, engine vibrations, and rapid error drift, causing navigation systems to break down when they are needed most.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Raw smartphone IMU data (3-axis accelerometer, 3-axis gyroscope, magnetometer), sparse GNSS position feeds, offline OpenStreetMap road network data, and benchmark datasets like IO-VNBD.
In-vehicle sensor orientation alignment, deep learning-based velocity estimation and vibration filtering, Extended/Unscented Kalman Filtering for sensor fusion, non-holonomic constraint enforcement, and Hidden Markov Model map-matching.
Continuous real-time vehicle positioning update at 10Hz, smooth map trajectory visualization on mobile UI, instant seamless switching between GNSS and dead reckoning modes, and drift restricted to <10% of total travel distance.
CONSTRAINTS
- Must run completely offline on device during GNSS outages without external OBD-II vehicle speedometer cable connections.
- Must execute real-time inference on consumer smartphone hardware within strict latency limits (<100ms per loop).
- Must automatically calibrate for arbitrary phone mount orientations (dashboard, holder, pocket, angle changes).
JURY METRICS
- Positional drift of less than 5 meters over a 50-meter GNSS-denied environment in under 1 minute.
- Positional drift of less than 100 meters over a 1km tunnel/underground environment at 60 kmph.
- Sensor fusion update rate of 10Hz on mobile and ~200Hz on edge engine with FOG-based IMU sensors.
CORE BOTTLENECK
Isolating high-frequency engine vibrations and pothole shocks from actual vehicle forward motion using only consumer-grade smartphone accelerometers without accumulating catastrophic dead-reckoning drift.
Why You Should Choose This PS (Pros)
- High national strategic relevance aligned with ISRO's indigenous navigation initiatives (NavIC / Indian Space Programme).
- Clear, quantifiable performance benchmarks making evaluation objective and straightforward.
- Enormous commercial potential across logistics, delivery, and transport sectors in India.
Challenges & Risks to Watch (Cons)
- High mathematical complexity involving sensor fusion (EKF/UKF), deep learning timeseries models, and map matching algorithms.
- Handling arbitrary smartphone orientations and noise profiles inside chaotic Indian road conditions is notoriously tricky.
- Crowded domain if teams rely purely on standard library Kalman filters without strong AI speed estimation.
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
Background Vehicle logistics, ride-hailing services, quick commerce and emergency responders heavily rely on smartphone-based navigation apps (like Google Maps or MapmyIndia) powered by GNSS (GPS/Galileo/NavIC etc). However, when a vehicle enters a long underground tunnel/ underpass, a multi-level parking lot, a dense forested highway, or a deep urban canyon surrounded by skyscrapers, GNSS connectivity drops entirely. GNSS signals are inherently weak and vulnerable to structural blockage (urban canyons, dense foliage, tunnels, deep valleys) and unintentional electromagnetic interferences from variety of sources such as jamming. This causes navigation apps to freeze, jump erratically, or miscalculate upcoming turns, leading to missed exits, delivery delays, and safety hazards.
In these environments, systems must rely on self-contained Inertial Navigation Systems (INS) built from Inertial Measurement Units (IMUs) (accelerometers and gyroscopes) to calculate position via dead reckoning during GNSS outage and switch back to GNSS aided INS after blackout. While INS is immune to external jamming, low-cost tactical or MEMS-grade IMUs suffer from inherent sensor biases, deterministic errors, and thermo-mechanical noise. While modern high-end cars possess factory-fitted, wheel-connected Inertial Navigation Systems (INS), the vast majority of vehicles on Indian roads—including commercial trucks, older cars, and millions of two-wheelers (motorcycles/scooters)—rely solely on the driver’s smartphone mounted on the dashboard or placed in mobile holder.
Using a smartphone's internal MEMS IMU (accelerometer and gyroscope) to track vehicle position via dead reckoning during a GNSS blackout is highly challenging. The smartphone is subjected to severe chassis vibrations, engine harmonics, sudden braking, and road potholes. Without an external speedometer feed from the vehicle's OBD-II port, calculating distance and velocity exclusively from consumer-grade smartphone sensors results in exponential error accumulation, causing the estimated location to drift away within seconds. To overcome these challenges, there is a need for AI-ML enhanced dead reckoning and sensor fusion(GNSS+INS) techniques that integrate AI and machine learning models with real-time correction strategies.
Description The goal is to develop a lightweight, edge-deployable software engine and mobile application that transforms a standalone smartphone into an Intelligent Dead Reckoning (IDR) system with GNSS Fusion. When a GNSS outage occurs, the application must instantly transition to inertial tracking(INS), maintaining lane-level accuracy without requiring any physical connection to the vehicle’s internal computer and seamlessly switch back to GNSS aided INS solution.
To bypass the need for an external speedometer, the solution must employ AI/ML models trained on vehicle kinematics to accurately predict vehicle speed and acceleration profiles solely from the smartphone’s noisy accelerometer/gyro inputs. It must dynamically detect and filter out non-navigation motions such as engine idling vibrations, pothole shocks, bumps, and accidental phone misalignments on the mount.
Furthermore, the navigation engine should implement a smart Map-Matching Filter. By overlaying the inertial trajectory onto an offline map database (e.g., Open Street Map), the system should use the road layout as a constraint. For instance, it can apply Non-Holonomic Constraints (NHC), assuming a car cannot slide sideways or fly upwards, to dramatically snap the drifting IMU path back onto the actual road grid.
Also, the GNSS+INS fusion Algorithm should employ AI/ML techniques to develop an AI based fusion model to mitigate drift errors and provide accurate position.
The Final solution and AI/ML models developed should not be constricted to smart phone IMU sensors data alone (Mobile application). These algorithms/models should also work with any other external IMU sensors data (Edge deployable software engine).
Dataset Details: IO-VNBD: Inertial and Odometry benchmark dataset for ground vehicle positioning.
This dataset should be used to train & test the models and submit for screening of proposals. Teams are required to include the preliminary AI models and the results of the position plot inferenced from the subset of IO-VNBD dataset as part of their proposals submitted for evaluation. During the screening process more datasets will be provided for further evaluation of the AI models.
The On-Device Workflow Dead reckoning and GNSS fusion algorithms are hybrid. Complex training happens in the cloud/desktop apriori, while inference happens on the smartphone.
Expected Solution The final deliverable must be a working mobile application and an Edge deployable software engine exhibiting the following technical capabilities:
Performance Benchmark:
Dead Reckoning: The solution must restrict positional drift to less than 10% of the total distance travelled using smartphone IMUs sensors during GNSS signals blackout (for e.g., in case of smartphones IMU, a drift of less than 5 meters is desired over 50m GNSS denied environment in <1 minutes OR less than 100m of drift over a 1km GNSS denied environment at a speed of 60kmph in tunnels/underground metro OR similar simulated environments where GNSS signals are unavailable).
GNSS+INS Fusion: Position update rate of 10Hz with processing on smartphones (Mobile application) and higher update rates on Edge deployable software engine using FOG based IMU sensors data (around 200Hz).