AI-ML based Intelligent Dead Reckoning system for seamless navigation

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.

1. Model Training (Cloud/Desktop): Teams should train AI-ML models using IMU sensors datasets collected using a smartphone mounted on their vehicles or datasets available in open-source domain (for eg. ‘IO-VNBD dataset’) and bring trained models with them for SIH finale. Teams can bring the downloaded map database (e.g., Open Street Map).

2. On-Device Execution (Smartphone): During SIH Finale, the trained, lightweight model will need to be exported to the smartphone. It should receive live inputs from the phone's built-in Inertial Measurement Unit (IMU)-the accelerometer, gyroscope, and magnetometer/compass and GNSS data if available. The AI-ML based algorithms should remove IMU sensors noise & bias, predict corrections, perform map matching and determine continuous position using AI-ML based algorithms for both dead reckoning and GNSS+INS Fusion. For complete details regarding final solution, refer to the expected solution section.

Expected Solution The final deliverable must be a working mobile application and an Edge deployable software engine exhibiting the following technical capabilities:

• In-Vehicle Alignment & Calibration Engine: An algorithmic module that automatically determines the phone’s pitch, roll, and yaw relative to the vehicle's driving direction, whether the phone is strictly dashboard-mounted or placed in mobile holder.
• AI Speed & Vibration Filter: A deep-learning or statistical signal-processing model running locally on the phone that filters out high-frequency road noise/potholes and directly estimates vehicle forward velocity from IMU signals.
• Advanced Map-Matching & Kinematic Constraints: A framework (e.g., AI-ML framework or Unscented Kalman Filter + Hidden Markov Map Matching) that binds the calculated position to known road networks and geometric paths during a dropout.
• GNSS+INS Fusion Engine: An innovative AI based Sensor Fusion Algorithm that combines GNSS & IMU measurements and provides significant improvement in overall output by eliminating drift errors and providing accurate position and velocity.
• Seamless GNSS Deficit Handler: An instant seamless transition mechanism between GNSS aided INS and Dead reckoning modes within milliseconds of GNSS signal blackout and vice-versa.
• Real-time Navigation Interface: A functional mobile application with UI displaying a smooth, uninterrupted vehicle icon showing seamless navigation.

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).