The need for safety in Automated Driving (AD) is becoming increasingly critical with the accelerating deployment of this technology. Beyond functional safety, industry must guarantee the operational safety of automated vehicles. Towards that end, Mobileye introduced the Responsibility Sensitive Safety (RSS), a model-based approach to Safety [1]. In this paper we expand upon this work introducing the C++ Library for Responsibility Sensitive Safety, an open source executable that implements a subset of RSS. We provide architectural details to integrate the C++ Library for Responsibility Sensitive Safety with AD Software pipelines as safety module overseeing decision making of driving policies. We illustrate this application with an example integration with the Baidu Apollo AD stack and simulator, [2] and [3], that provides safety validation of the planning module. Furthermore, we show how the C++ Library for Responsibility Sensitive Safety can be used to explore the usefulness of the RSS model through parameter exploration and analysis on minimum safe longitudinal distance, (dmin), considering different weather conditions. We also compare these results with half-of-speed rule followed in some parts of the world. We expect that the C++ Library for Responsibility Sensitive Safety becomes a critical component of future tools for formal verification, testing and validation of AD safety and that it helps bootstrap the AD research efforts towards standardization of safety.

Proper navigation of walking machines in unstructured terrain requires the knowledge of the spatial position and orientation of the robot. There are many approaches for localization of mobile robots in outdoor environment, but their application to walking robots is rather rare. In particular, middle sized robots like LAURON III don’t provide the possibility to carry large or heavy sensors. Due to many degrees of freedom of walking robots the localization task becomes a even more complex challenge. This paper discusses the problem, presents a method of resolution and describes the first steps towards a localization system for the six-legged walking robot LAURON III.

Locomotion of walking machines on a well defined path in unstructured terrain requires a model of the environment. But, in particular, middle sized robots like LAURON III don't provide the possibility to carry large or heavy sensors. This paper focuses on generating a 3D map of unstructured environment on the basis of sparse sensory information. In respect of walking robots this covers at first the selection of the next footsteps. For this purpose the advanced inference grid is introduced as a variant of the vector field histogram for the representation of the environment.