AI for Advanced Driver Assistance Systems (ADAS) |…

Design and validate ADAS features with AI-driven perception, decision

ADAS features such as AEB, ACC, LKA, and lane change support rely on robust perception, prediction, and control. AI improves detection and decision-making but must be paired with functional safety, SOTIF, and extensive validation.

Enhance driver safety and comfort while ensuring explainable, validated behavior and clear driver communication.

Perception (det/seg/track), fusion, lane/road modeling, intent predict

Embedded ECUs/SoCs, HMI alerts, OTA pipelines, diagnostics.

Data engine, scenario mining, simulation, safety cases, A/B and canary OTA with rollback.

live demo · related simulation● LIVE

Lane centering, lane change assist with driver intent.

Drowsiness/distraction detection; HMI alerts.

Implementation Blueprint

Frequently asked questions

What are the key considerations for ensuring safety when integrating AI into ADAS systems?

ISO 26262/SOTIF; safety cases; hazard analysis.

How can Over-the-Air (OTA) updates be secured to protect against vulnerabilities and maintain system integrity?

Secure OTA, auth, intrusion detection, UNECE R155/R156.

Why is it important to collect and analyze logs and event data from ADAS systems for post-incident analysis and system improvement?

Logs, event data recorders, and explainable behavior.

What regional standards and protocols must be considered when developing and deploying ADAS features?

Meet regional ADAS rules, NCAP protocols, and labeling.

▶ Try it live

Everything above runs in your browser — open River Network Formation and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open River Network Formation simulation