Cariad SE

Techniques for generating, updating, and utilizing highly detailed digital maps that include lane-specific information, and for precisely determining a vehicle's position within these lanes, often using sensor data.

Systems and methods for enhancing the safety of vulnerable road users (pedestrians, cyclists) by improving their detection, prediction, and precise localization relative to the vehicle, often leveraging communication technologies and specialized markers.

Methods and apparatus for detecting objects and determining their three-dimensional position and orientation (pose) using imagery or point cloud data, often for navigation, surveying, or environmental understanding.

Algorithms and systems for planning and executing complex vehicle maneuvers, often involving cooperation with other vehicles or infrastructure, to optimize traffic flow, avoid collisions, or navigate challenging scenarios. This includes lane changes, cut-ins, and traffic congestion.

Techniques and hardware for autonomous systems to gather and interpret data about their surroundings, including obstacle detection, object recognition, and depth estimation, to inform control decisions.

Techniques utilizing deep learning models like Generative Adversarial Networks (GANs) or diffusion models to create new images, modify existing ones, or generate synthetic data based on various inputs or conditions.

Algorithms and systems for generating, optimizing, and executing trajectories for autonomous vehicles or robots to move through an environment, often involving obstacle avoidance, route validation, and goal reaching.

Techniques for generating human-like text or other content using large pre-trained models, often involving prompt engineering, speculative decoding, or multi-modal inputs for content creation.

Integrated systems for managing parking facilities, guiding vehicles to available spots, and providing notifications, often leveraging sensors, communication, and remote control.

Technologies for monitoring vehicle performance, detecting faults, collecting operational data, and providing remote assistance or automated control based on sensor inputs and network connectivity.

Techniques for combining data from disparate sensor types (e.g., cameras, radar, mobile device signals) to achieve a more robust and comprehensive understanding of an environment or subject, often leveraging machine learning for interpretation and correlation.

Technologies for detecting adverse weather conditions or road hazards and providing timely alerts and adaptive control strategies to vehicles and drivers to enhance safety.

Methods and components for optimizing the performance, efficiency, and control of electric motors, inverters, and power converters within electric vehicle and hybrid vehicle drivetrains.

Systems that monitor a vehicle operator's physiological state, attentiveness, or behavior using in-cabin sensors and machine learning to enhance safety or personalize vehicle functions.

Systems for coordinating and controlling fleets of autonomous vehicles or machines, including task allocation, route optimization, and monitoring their operational status and progress.

Systems and methods for dynamically adjusting traffic signal timings and phases at intersections based on real-time traffic conditions, priority vehicles, and predictive analytics to optimize flow and reduce congestion.

Methods and apparatus for improving the visual fidelity, resolution, or compression efficiency of video signals, often through advanced processing, up-scaling, or neural network-based filters.

Techniques and architectures for ensuring the reliability, fault tolerance, and performance validation of autonomous driving systems, including redundant computing platforms and perception system monitoring.

Methods and systems for efficiently allocating computing resources, balancing workloads, and managing power states to improve performance, reduce energy consumption, or enhance reliability in computing platforms.

Methods for training machine learning models across multiple decentralized devices or servers while keeping data localized, often involving aggregation of model parameters and secure communication.

Systems and methods that leverage location data, IoT sensors, and predictive analytics to optimize urban services such as traffic flow, emergency response, parking, and waste collection.