Edge AI for autonomous vehicles
An ADAS data training system that runs inside the vehicle.
We have built the entire system, from sensor data to a trained model. ALICE, our edge-AI model, perceives live data inside the vehicle and runs the pipeline there: processing, labelling, monitoring, quality checks and training. It only observes, and it never touches the control path.
See how it works
01 Observe
ALICE observes. It never controls.
ALICE sits beside your existing stack and reads the sensor streams live. It never writes to the vehicle and is never in the control path.
- Read-only tap on the sensor streams
- Runs on edge hardware in the vehicle
- Camera and radar
02 Understand
Annotation happens on the vehicle.
Vehicles, pedestrians, signals, lanes and drivable space are labelled on the vehicle itself, so every frame is already understood by the time it leaves the car.
03 Encode
Raw sensor streams become latent records.
Our multimodal encoder keeps what a perception model learns from and leaves the redundant bulk behind. The output is a compact, versioned record, not video.
1 · Raw frame
2 · Processed representation
Object Vehicle Turn right 10 m
3 · Tokens & features
04 Learn
One closed loop, from sensor to fleet.
Processing, labelling, monitoring, training, simulation and validation are one system, running on the vehicle’s edge computer, so learning from the road does not wait on a long collect, upload, train and redeploy cycle.
Sensor data
Camera streams are read from the vehicle, read-only.
Encode to latents
Our multimodal encoder, ALICE, turns raw streams into compact latent records.
Process, label & monitor
Records are structured, versioned, labelled and quality-checked as they are produced.
Train at the edge
ALICE learns directly from the records, on the vehicle.
Simulate & validate
Every new model is proven in simulation and on logged scenes before it returns to the fleet.
Impact
What changes for your team.
Quality checking
Catch blurred, blocked or drifting sensor data at the source, before it ever reaches a training set.
Scene searching
Find the moments that matter, such as a pedestrian at dusk, a cut-in or a faded lane marking, across every drive.
Monitoring
Watch data health and model behaviour continuously, and see when the road changes under a model.
Autolabelling
Objects, lanes and signals are labelled on the vehicle, so human review goes to the hard cases.
Architecture
Your driving stack stays unchanged.
Your driving stack · unchanged
Sensors
Perception
Planning
Control
read-only copy
ALICE sidecar · observes only
Observe
reads the streams
Annotate
objects, lanes, signals
Encode
latent records
Learning loop
ingest · train · simulate · validate
How we are different
Where the approach differs.
Common industry approach
The BharatNex approach
Record everything, upload it, sort it out later.
Understand on the vehicle. Keep what a model learns from.
Labels come after the fact, from large manual teams.
Autolabelling at the edge. People review the hard cases.
A long cycle: collect, upload, label, train, redeploy.
A shorter loop: perceive, label and train on the vehicle.
Raw footage is decoded and prepared again for every experiment.
Versioned latent records, ready to train on.
Tooling tied to one stack, or one stage of the pipeline.
A read-only sidecar beside your stack. Nothing in your control path changes.
Why BharatNex
What BharatNex offers your team.
Better
Data arrives understood: quality-checked, annotated and searchable from the first frame.
Faster
Learning starts at the vehicle, not after a bulk upload and a manual labelling queue.
Effective
Compute and human attention go to the scenes where a model is weak, not to hours of empty road.
Different
The whole pipeline, sensor to fleet, as a sidecar. Not one more tool to bolt on.
We measure every claim against your own baseline, on your own data, together with your team.
FAQ
Questions about the training system.
What is the BharatNex ADAS data training system?
It is a complete pipeline for training ADAS models that runs inside the vehicle. ALICE reads the sensor streams, labels the scene and encodes it into compact latent records, and the same system processes the data, trains, simulates and validates models before they return to the fleet. BharatNex has built the whole system, from sensor data to a trained model, so a team does not have to join separate tools for each stage.
What is ALICE?
ALICE (Automotive Latent Intelligence Compression Engine) is BharatNex's edge AI multimodal encoder. It runs on edge hardware in the vehicle, reads camera and radar streams read-only, labels objects, lanes and signals, and turns raw frames into compact, versioned latent records that a model can be trained on.
How is ALICE different from a video or multimedia encoder?
A video or multimedia encoder, such as H.264, is built so people can watch the result. A multimodal encoder is built so a perception model can learn from it. ALICE keeps what a model learns from and leaves the redundant bulk of raw footage behind. The output is a compact, versioned record, not video, so it can be searched and trained on without decoding and preparing raw footage again for every experiment.
Why run ADAS training and data processing on the vehicle?
The common approach is to record everything, upload it, label it later and retrain elsewhere, which makes a long cycle. Running perception, labelling, quality checks and learning on the vehicle's edge computer shortens that loop, so learning from the road does not wait on a bulk upload and a manual labelling queue.
Does ALICE control the vehicle or change our driving stack?
No. ALICE is a read-only sidecar. It sits beside your existing stack, reads a copy of the sensor streams, never writes to the vehicle and is never in the control path. Your sensors, perception, planning and control stay as they are. ALICE is a data tool and makes no safety or regulatory-compliance claims.
What can a team do with it?
Four things: quality checking, which catches blurred, blocked or drifting sensor data at the source; scene searching, which finds moments such as a pedestrian at dusk, a cut-in or a faded lane marking across every drive; monitoring of data health and model behaviour; and autolabelling of objects, lanes and signals on the vehicle, so human review goes to the hard cases.
Which sensors does ALICE work with?
ALICE reads camera and radar streams. Performance depends on the vehicle, the sensors and the data, so BharatNex measures every claim against your own baseline, on your own data, together with your team.
How can we request a demo or collaborate?
Use the enquiry form on this page, or write to [email protected]. Say who you are and what you are looking for, and suggest a day and time for a demonstration. Related: V2X and V2V communication from BharatNex.
Get in touch
Collaborate, or see the technology.
Tell us who you are and what you are looking for: a product demo, a collaboration, both, or just a conversation. It takes a minute, and you can suggest a day and time for a demo.
Open the enquiry form
For anything else, write to [email protected].
Indore, India · © BharatNex Energy Private Limited · Patent application filed. ALICE is a read-only data tool and makes no safety or regulatory-compliance claims. Performance depends on the vehicle, sensors and data.