Can you help with ADAS and autonomous driving?

Yes. We build perception, sensor fusion, prediction and planning components, and the simulation setup to test them. We work alongside your system, safety and platform teams rather than replacing them.

How does predictive maintenance work?

We combine telemetry, sensor streams and service history to detect anomalies and estimate remaining useful life, then turn that into alerts and service planning. A pilot fleet proves the value before a full roll-out.

Can AI inspect parts on the production line?

Yes. Vision models can detect surface defects, weld and paint issues, missing parts and assembly errors at line speed, with images and results stored for traceability.

Do you support electric vehicles and batteries?

Yes. We build state-of-health and range prediction, smart-charging optimisation and degradation models for EV and battery programmes.

Which sensors do you work with?

Cameras, radar, LiDAR, ultrasonic sensors, IMU, GNSS and CAN-bus data. We fuse them where it improves robustness and calibrate for each vehicle platform.

Do the models run inside the vehicle?

Yes. We optimise models for the target SoC with quantisation, pruning and runtimes such as TensorRT or ONNX Runtime, and test against latency, memory and power budgets.

How do you handle night, rain and glare?

Through diverse data collection, augmentation, synthetic data and sensor fusion, and by reporting accuracy per condition so weak spots are visible instead of averaged away.

Can you integrate with AUTOSAR, ROS 2 or Android Automotive?

Yes. We integrate with the middleware and operating systems your platform uses and deliver components with clean interfaces and documentation.

How do you test automotive AI?

With scenario-based simulation, software-in-the-loop and hardware-in-the-loop testing, closed-track trials and shadow-mode runs on pilot fleets before any customer exposure.

Are your systems ISO 26262 certified?

Compliance applies to the whole system and the organisation that builds it. We design our processes and evidence to align with ISO 26262 and SOTIF and support your safety case, while certification stays with you and your assessors.

How do you approach cybersecurity?

We apply threat modelling, secure coding, signed and staged OTA updates and access control, aligned with ISO/SAE 21434 and UNECE R155 / R156 expectations.

How is driver and location data protected?

Sensitive in-cabin and location data is processed on the device where possible, encrypted in transit and at rest, and governed by consent and retention rules aligned with GDPR.

Who owns the code, models and data?

You do. Source code, trained models and datasets are yours, and we document them so your teams can maintain and extend them.

Should AI make safety-critical decisions on its own?

We typically use AI for perception and prediction inside a deterministic safety envelope with defined fallbacks. The right split depends on your safety goals and regulation, and we agree it during discovery.

How long does a proof of concept take?

A focused proof of concept usually takes 6 to 10 weeks. Production-grade programmes with edge integration and validation typically run 6 to 12 months, depending on scope and safety requirements.

How much does it cost?

Cost depends on sensors, use case, data volume, target hardware and validation needs. After discovery we provide a fixed-scope estimate for the proof of concept and a phased budget for production.

Yes, and we recommend it. Start with one use case, such as a perception module or a predictive-maintenance pilot, and expand once the measured results justify it.

What happens after launch?

We provide monitoring, model retraining from fleet data, OTA update support and a roadmap based on real-world performance.

Do you work with start-ups as well as large OEMs?

Yes. We scope engagements to fit, from a lean EV start-up pilot to a multi-programme partnership with an OEM or tier-1 supplier.

AI in Automotive Industry

AI that makes every vehicle smarter, safer and connected

From ADAS perception and driver monitoring to predictive maintenance and smart factories, Aarvexa engineers automotive AI that runs on the edge, inside the vehicle and across the whole fleet.

ADAS & perception
Edge-ready models
Safety-first engineering

Talk to our experts See how it works

LIVEADAS PERCEPTION28 ms · 30 FPS

ACCLKAFCWAEB

TTC 3.1 s

AI decision

Lane centred, cruising

Speed

62km/h

limit 60ACC on

Driver attention

Eyes on road

78%312 km

ADAS PerceptionSensor FusionDriver MonitoringPredictive MaintenanceVisual Quality InspectionVoice AssistantsEV Battery IntelligenceFleet AnalyticsEdge AISimulation & ValidationOTA UpdatesFunctional SafetyADAS PerceptionSensor FusionDriver MonitoringPredictive MaintenanceVisual Quality InspectionVoice AssistantsEV Battery IntelligenceFleet AnalyticsEdge AISimulation & ValidationOTA UpdatesFunctional Safety

What we build

AI for every part of the automotive value chain

From the sensor on the bumper to the dashboard in the plant, we build complete systems, not just demos.

Perception

ADAS & Driver-Assist Perception

Lane, vehicle, pedestrian, sign and free-space detection from camera, radar and LiDAR, tuned for real roads and real weather.

Autonomy

Autonomous Driving Software

Sensor fusion, localisation, motion planning and control stacks, proven in simulation before they ever touch asphalt.

In-cabin AI

Driver & Cabin Monitoring

Drowsiness, distraction and occupancy sensing with on-device models that keep cabin data private.

Vehicle health

Predictive Maintenance

Telematics and sensor data turned into early warnings for brakes, batteries, tyres and powertrains.

Industry 4.0

Smart Manufacturing & Quality

Vision inspection, defect detection and process analytics for plants, tier-1 suppliers and body shops.

Connected

Connected Car & Voice Assistants

In-car voice, personalisation and OTA-ready services for infotainment and companion apps.

Electrification

EV & Battery Intelligence

State-of-health prediction, charge optimisation and range estimation that adapt to the driver and the climate.

Mobility

Fleet & Mobility Analytics

Route, utilisation and risk analytics for OEM, rental, logistics and ride-hailing fleets.

Live pipeline

How automotive AI senses, decides and acts

Five stages turn raw sensor data into safe, explainable behaviour. Hover to hold a stage, click to jump.

Step 01 of 5

Sense

Cameras, radar, LiDAR, ultrasonic and CAN-bus signals stream the world around the vehicle.

Multi-sensor, time-synchronised
Calibrated per vehicle platform
Rain, glare and low-light ready

A full 360° view of the road

Where it applies

One AI platform, six automotive applications

Pick the programme that matches your business. We reuse the same proven perception, prediction and data core.

Perception and planning you can validate

Detect, track and predict everything around the vehicle, then plan safe responses that stand up to simulation and road testing.

Lane keeping and lane-change assist
Pedestrian and vehicle collision avoidance
Adaptive cruise with cut-in handling
Parking and low-speed manoeuvres

Plan this project

HighwayADAS status

96%confidence

Lane keepingActive
Adaptive cruise62 km/h
Collision warningArmed

Open report

Live from the vehicle on

Synced to fleet cloud

Why Aarvexa

A partner that takes safety as seriously as you do

Automotive buyers need AI that is accurate, explainable and maintainable for years. We pair modern machine learning with engineering discipline and the systems around the vehicle.

Who we work with

OEMsTier-1 & Tier-2 suppliersFleet operatorsMobility platformsDealer networksEV start-ups

AI with automotive discipline

Requirements, traceability, test evidence and documented models, so your safety and quality teams can work with us.

Built for the edge, not just the cloud

Models compressed and tuned for in-vehicle SoCs with strict latency, memory and power budgets.

Data and simulation at scale

Labelling, synthetic data and scenario simulation to cover the rare cases that real roads rarely hand you.

From prototype to production

Proof of concept, pilot fleet, validation and roll-out handled by one delivery team.

Technology

A stack built for the vehicle, proven on the road

We choose the right tool for each layer, from the sensor model to the fleet cloud, and swap parts when your platform changes.

Perception & ML

YOLO / RT-DETRSemantic segmentationBEVFusionPointPillarsPyTorchTensorRT

Edge & vehicle compute

NVIDIA DRIVE / JetsonQualcomm Snapdragon RideRenesas R-CarONNX RuntimeQNXAutomotive Grade Linux

Vehicle software

AUTOSAR AdaptiveROS 2CAN / CAN-FDAndroid AutomotiveC++ / RustOpenSCENARIO

Data & simulation

CARLANVIDIA OmniverseSynthetic dataLabelling pipelinesMLflowKubernetes

Connectivity & cloud

V2XMQTTAWS IoT FleetWiseAzure IoTOTA updatesTelematics APIs

Safety & trust

Engineered for safety, security and trust

Automotive software is judged by what happens when things go wrong. We design for that from day one.

Functional safety mindset

Processes and evidence designed to align with ISO 26262 and SOTIF (ISO 21448) for safety-related functions.

Cybersecurity by design

Threat modelling and secure OTA aligned to ISO/SAE 21434 and UNECE R155 / R156 expectations.

Data privacy

In-cabin, location and driving data processed on the device where possible, with consent and GDPR-aware retention.

Rigorous validation

Simulation, software-in-the-loop, hardware-in-the-loop and closed-track testing before any road exposure.

Our process

From first idea to a fleet on the road

Indicative timelines for a typical proof of concept through pilot. We confirm yours after discovery.

01Week 1–2

Discovery & use-case definition

Operating domain, sensors, vehicles, success metrics and safety goals agreed before any modelling starts.

02Week 2–5

Data & simulation setup

Data collection, labelling pipelines, synthetic data and a scenario library that covers the cases that matter.

03Week 4–10

Model development

Perception, prediction or analytics models trained, benchmarked per condition and documented.

04Week 8–16

Edge integration

Compression, optimisation and integration on the target SoC and middleware, with latency and power budgets met.

05Week 14–22

Validation & pilot

SIL/HIL testing, closed-track and pilot-fleet trials, with measured results and a safety evidence pack.

06Ongoing

Deploy & improve

Release, OTA updates, fleet monitoring and continuous retraining from real-world edge cases.

FAQ

Frequently Asked Questions

Straight answers on automotive AI: applications, technology, safety, cost and timelines.

It is the use of machine learning, computer vision and data analytics to make vehicles, factories and fleets smarter: from ADAS and driver monitoring to predictive maintenance, quality inspection, connected services and EV battery management.

End-to-end automotive AI: data and simulation pipelines, models, edge deployment on in-vehicle hardware, integration with vehicle and cloud systems, and validation evidence your engineering and safety teams can use.

OEMs, tier-1 and tier-2 suppliers, fleet and logistics operators, mobility platforms, dealer networks, insurers and EV start-ups.

Both. We start from proven architectures and tune them on your sensors, vehicles and operating domain, so performance is measured on your roads rather than on a generic benchmark.

Book a free consultation. We learn about your use case, show a live perception demo and propose a scoped proof of concept with clear success metrics.

Let's build it

Ready to put AI in the driver's seat?

Book a free consultation. We will show a live perception demo and map out a proof of concept you can test on your own vehicles and data.

Book a free consultation Explore our AI services