PROSENSE: PROactive SENSing for autonomous driving

Purpose and goal:

Despite rapid research & innovation in scene perception for autonomous driving, many challenges remain open in understanding complex traffic scenes with occluded road users. To overcome these challenges, Prosense aims to extend the state-of-the-art by: 1. Incorporating multi-sensor information to create a scene representation that includes possible occlusions.

2. Enhancing the robustness of scene perception in different traffic scenarios.

3. Integrating scene metadata for context-aware detection & classification of occluded road users, object anticipation & prediction. Expected results and effects:

At the end of the project, the advancements in key technology areas will extend the scene perception capabilities.The main results of this project are: 1. Developed algorithms for handling occlusions based on multi-modal sensor data and scene metadata.

2. Integration of advanced algorithms for scene perception in the presence of occluded objects on-board an autonomous research vehicle. 3. Pubic demonstration of integrated algorithms emulating complex traffic occlusions. Approach and implementation:

The project will start by focusing on the two main project enablers, generating synthetic data and developing a 3D occlusion model. Meanwhile, fundamental research on multi-source fusion for robust real-time scene perception and proactive context aware object perception will be conducted. This results in continuous development & assessment of algorithms.

The outcome will be published in reputable conferences and journals. As new algorithms & functionalities are developed, they will be integrated in autonomous driving platform for timely demonstration on a public test track.