Ghost targets and multipath reflections cause problems with the functionality and performance of radar-based ADAS.

Idiada has undertaken a major expansion of its Spanish proving ground facilities, marking a significant investment in future mobility testing infrastructure. The new development, scheduled for completion in November 2025, focuses on enhancing the company’s ADAS and CAV testing capabilities.

Autonoma will showcase AutoVerse in Stuttgart, which the company describes as a cutting-edge simulation platform designed to accelerate AV/ADAS development. AutoVerse provides a high-fidelity, physics-driven virtual world where developers can test, validate and optimize autonomous systems at scale, without real-world risks.

Wayve develops AI models for automated driving. Founded in 2017, the company pioneered AV2.0, a scalable, data-driven approach to automated driving using end-to-end AI in a way that fundamentally differs from other conventional self-driving systems.

ADAS requires unprecedented sensor performance and reliability. As vehicles advance toward full autonomy, optical sensors must consistently identify critical situations under all conditions, remaining unaffected by stray light that can momentarily impair vision and jeopardize safety.

Switching between interfaces to label multi-sensor data and multiple modalities leads to inconsistencies and reduces model performance. At the expo, Segments.ai will introduce its improved multi-sensor labeling platform. Labeling all sensor data in a single interface ensures consistency across time and modalities.

intoPIX is a leading provider of innovative image and video compression technologies. According to the company, its codecs are the most reliable on the market and the only ones managing image sensor data. intoPIX enables bandwidth-efficient connectivity by leveraging ethernet cables, ethernet PHY, MIPI, GMSL and other transmission technologies.

Eximia guides customers’ ADAS development teams through every step, from setting up test systems to commissioning, coding and executing tests.

Autonomous vehicles are rapidly becoming a reality, but their scalability remains challenging due to the complexity of the real world and regional differences in traffic behavior. Overcoming these challenges requires realistic simulation with intelligently interacting traffic agents.

RoboSense has launched a new version of the Reference System, an advanced data acquisition, ground truth generation and validation toolchain designed for autonomous driving and ADAS perception systems.

Anyverse will showcase Anyverse In-Cabin, its powerful synthetic data generation application for in-cabin monitoring AI. Completely web-based, this solution enables OEMs and suppliers to generate high-quality, regulation-ready datasets in just a few clicks, streamlining AI model training and validation.

aiMotive will showcase its advance in autonomous vehicle simulation at the expo. The aiSim World Extractor is an AI-powered tool designed to automate large-scale, high-fidelity map creation like never before.

Modern vehicles are increasingly complex, and auto makers require shorter development times (time-to-market). Vehicle owners now expect over-the-air (OTA) updates that provide bug fixes and new features throughout the product’s lifecycle. Addressing these challenges requires an overarching simulation layer to efficiently manage the growing amount of data.

Key innovations include ASAM CMP (Capture Module Protocol) support, which enhances communication between capture modules and centralized recording units, enabling users to combine the benefits of distributed and centralized datalogging systems. Xylon will also unveil a new pluggable GVIF video I/O module for recording from up to four cameras, along with full playback capabilities.

Kognic enhances annotation productivity by integrating automated and human-in-the-loop models, ensuring high-quality datasets with reduced costs.

InoNet will showcase its advanced datalogging solutions at the expo. Modern vehicles generate vast amounts of sensor data, requiring high-speed acquisition, processing and secure storage – especially in ADAS and autonomous driving (AD) testing. However, traditional dataloggers often struggle with latency, bandwidth limitations and space constraints.