Dynamic Occupancy Grid Mapping is a technique used to generate a local map of the environment, containing both static and dynamic information. Typically, these maps are primarily generated using lidar measurements. However, with improvements in radar sensing, resulting in better accuracy and higher resolution, radar is emerging as a viable alternative to lidar as the primary sensor for mapping. In this paper, we propose a radar-centric dynamic occupancy grid mapping algorithm with adaptations to the state computation, inverse sensor model, and field-of-view computation tailored to the specifics of radar measurements. We extensively evaluate our approach with real data to demonstrate its effectiveness and establish the first benchmark for radar-based dynamic occupancy grid mapping using the publicly available Radarscenes dataset.

Safe autonomous driving in urban areas requires robust algorithms to avoid collisions with other traffic participants with limited perception ability. Current deployed approaches relying on Autonomous Emergency Braking (AEB) systems are often overly conservative. In this work, we formulate the problem as a partially observable Markov decision process (POMDP), to derive a policy robust to uncertainty in the pedestrian location. We investigate how to integrate such a policy with an AEB system that operates only when a collision is unavoidable. In addition, we propose a rigorous evaluation methodology on a set of well-defined scenarios. We show that combining the two approaches provides a robust autonomous braking system that reduces unnecessary braking caused by using the AEB system on its own.

This paper explores a methodology whereby accident data is directly used to develop a braking strategy for an autonomous emergency braking system. Future vehicles will be equipped with additional technologies. Detailed information about accidents or critical situations can be recorded. If enough recorded data from critical situations are available, such data could be used to improve Active Safety Systems. In our approach, we do not model the behavior of pedestrians or drivers. The idea is to use the capability of machine learning to get the behaviors out of traffic data. Machine learning is used to derive the function design for an emergency braking system for pedestrians. Generated traffic scenarios are used to review the methodology. Random Forests and Neural Networks are used for the function designs and the learned function designs are compared with a reference implementation.