Occupancy Grid Mapping for Automotive Driving Exploiting Clustered Sparsity

Occupancy grid maps provide information about obstacles and available free space in the environment and are crucial in automotive driving applications. An occupancy map is constructed using point cloud data from sensor modalities such as light detection and ranging (LiDAR) and radar used for automotive perception. In this article, we...

LiDAR and Radar-Based Occupancy Grid Mapping for Autonomous Driving Exploiting Clustered Sparsity

Occupancy grid maps are fundamental to autonomous driving algorithms, offering insights into obstacle distribution and free space within an environment. These maps are used for safe navigation and decision-making in self-driving applications, forming a crucial component of the automotive perception framework. An occupancy map is a discretized...

Sharpening the Future of Occupancy Grid Map Prediction Methods: An Investigation into Loss Functions and Semantic Segmentation Multi-Task learning for More Accurate OGM Predictions

For an Autonomous Vehicle (AV) to traverse safely in traffic, It is vital it can anticipate the behavior of surrounding traffic participants using motion prediction. Current motion prediction approaches can be categorized into object-centered and object-agnostic methods and are primarily based on deep learning. The former relies on a human...