Although many research vehicle platforms for autonomous driving have been built in the past, hardware design, source code and lessons learned have not been made available for the next generation of demonstrators. This raises the efforts for the research community to contribute results based on real-world evaluations as engineering knowledge of building and maintaining a research vehicle is lost. In this paper, we deliver an analysis of our approach to transferring an open source driving stack to a research vehicle.We put the hardware and software setup in context to other demonstrators and explain the criteria that led to our chosen hardware and software design. Specifically, we discuss the mapping of the Apollo driving stack to the system layout of our research vehicle, fortuna, including communication with the actuators by a controller running on a real-time hardware platform and the integration of the sensor setup. With our collection of the lessons learned, we encourage a faster setup of such systems by other research groups in the future.

Abstract-Autonomous vehicles operating in dynamic environments rely on precise localization. In this paper we present a novel approach for cooperative localization of vehicular systems and an infrastructure RADAR which is resilient against outliers generated from the RADAR. The problem of cooperative localization is represented as a factor graph, where interrelated topologies (including that of outliers) are added as constraint factor between vehicle states. Corresponding probabilities for multiple topologies between states of the two vehicles are calculated using the Probability Data Association Filter and assigned to the respective edges in the graph. Simulation results indicate that this technique has significant benefits in the context of improving the resilience against outliers while optimizing joint state estimates. The methodology presented in this paper has the potential to provide a robust and flexible framework for cooperative localization in the presence of clutter, obscuration and targets entering and leaving the field of view.

Abstract-This paper presents a novel and an improved approach for estimating the position of a vehicle using vehicleinfrastructure cooperative localization. In our previous work we presented a Factor Graph based solution which added the topology (inter-vehicle distance) as a constraint while localizing the vehicle using data from sensors from both inside and outside the vehicle. This paper extends the work by reducing the error in calculating the precision of the position by almost 27% in the best case and lowering the computational time by at least 50% over our previously proposed solution. This is achieved by modifying current topology constraints to be also dependent on the previous state estimate. The proposed solution remains scalable for many vehicles without increasing the execution complexity. Finally, simulations indicate that incorporating the new topology information via Factor Graphs can improve performance over the traditional, state of the art, Kalman Filter approach.

Abstract-This paper describes a new approach for cooperative localization by using both internal and external sensors. In contrast to the state-of-the-art methods, the proposed approach analyses the statistical properties of the systematic error during the transformation phase. A factor graph is formulated which jointly estimates both the biases and the locations. The proposed approach is evaluated by using simulated data from odometry, GPS and radar measurements. The experiment demonstrates excellent performance of the proposed approach in comparison to traditional techniques.