Nico Kaempchen

Experience, Results and Lessons Learned from Automated Driving on Germany's Highways

The BMW Group Research and Technology has been testing automated vehicles on Germanys highways since Spring 2011. Since then, thousands of kilometers have been driven on the highways around Munich, Germany. Throughout this project, fundamental technologies, such as environment perception, localization, driving strategy and vehicle control, were developed in order to safely operate prototype automated vehicles in real traffic with speeds up to 130 km/h. The goal of this project was to learn what technologies are necessary for automated driving. This paper presents the architecture and algorithms developed during this project, results from real driving scenarios, the lessons learned throughout the project and a quick introduction into the latest developments for improving the system.

Efficient occupancy grid computation on the GPU with lidar and radar for road boundary detection

Accurate maps of the static environment are essential for many advanced driver-assistance systems. A new method for the fast computation of occupancy grid maps with laser range-finders and radar sensors is proposed. The approach utilizes the Graphics Processing Unit to overcome the limitations of classical occupancy grid computation in automotive environments. It is possible to generate highly accurate grid maps in just a few milliseconds without the loss of sensor precision. Moreover, in the case of a lower resolution radar sensor it is shown that it is suitable to apply super-resolution algorithms to achieve the accuracy of a higher resolution laser-scanner. Finally, a novel histogram based approach for road boundary detection with lidar and radar sensors is presented.

Highly Automated Driving on Freeways in Real Traffic Using a Probabilistic Framework

A system, particularly a decision-making concept, that facilitates highly automated driving on freeways in real traffic is presented. The system is capable of conducting fully automated lane change (LC) maneuvers with no need for driver approval. Due to the application in real traffic, a robust functionality and the general safety of all traffic participants are among the main requirements. Regarding these requirements, the consideration of measurement uncertainties demonstrates a major challenge. For this reason, a fully integrated probabilistic concept is developed. By means of this approach, uncertainties are regarded in the entire process of determining driving maneuvers. While this also includes perception tasks, this contribution puts a focus on the driving strategy and the decision-making process for the execution of driving maneuvers. With this approach, the BMW Group Research and Technology managed to drive 100% automated in real traffic on the freeway A9 from Munich to Ingolstadt, showing a robust, comfortable, and safe driving behavior, even during multiple automated LC maneuvers.

Track-to-Track Fusion With Asynchronous Sensors Using Information Matrix Fusion for Surround Environment Perception

Driver-assistance systems and automated driving applications in the future will require reliable and flexible surround environment perception. Sensor data fusion is typically used to increase reliability and the observable field of view. In this paper, a novel approach to track-to-track fusion in a high-level sensor data fusion architecture for automotive surround environment perception using information matrix fusion (IMF) is presented. It is shown that IMF produces the same good accuracy in state estimation as a low-level centralized Kalman filter, which is widely known to be the most accurate method of fusion. Additionally, as opposed to state-of-the-art track-to-track fusion algorithms, the presented approach guarantees a globally maintained track over time as an object passes in and out of the field of view of several sensors, as required in surround environment perception. As opposed to the often-used cascaded Kalman filter for track-to-track fusion, it is shown that the IMF algorithm has a smaller error and maintains consistency in the state estimation. The proposed approach using IMF is compared with other track-to-track fusion algorithms in simulation and is shown to perform well using real sensor data in a prototype vehicle with a 12-sensor configuration for surround environment perception in highly automated driving applications.

Object existence probability fusion using dempster-shafer theory in a high-level sensor data fusion architecture

Future driver assistance systems need to be more robust and reliable because these systems will react to increasingly complex situations. This requires increased performance in environment perception sensors and algorithms for detecting other relevant traffic participants and obstacles. An objects existence probability has proven to be a useful measure for determining the quality of an object. This paper presents a novel method for the fusion of the existence probability based on Dempster-Shafer evidence theory in the framework of a highlevel sensor data fusion architecture. The proposed method is able to take into consideration sensor reliability in the fusion process. The existence probability fusion algorithm is evaluated for redundant and partially overlapping sensor configurations.

Fusion of laserscannner and video based lanemarking detection for robust lateral vehicle control and lane change maneuvers

The knowledge about lanes and the exact position on the road is fundamental for many advanced driver assistance systems. In this paper, a novel iterative histogram based approach with occupancy grids for the detection of multiple lanes is proposed. In highway scenarios, our approach is highly suitable to determine the correct number of all existing lanes on the road. Additionally, the output of the laserscannner based lane detection is fused with a production-available vision based system. It is shown that both sensor systems perfectly complement each other to increase the robustness of a lane tracking system. The achieved accuracy of the fusion system, the laserscannner and video based system is evaluated with a highly accurate DGPS to investigate the performance with respect to lateral vehicle control applications.

Lane-based safety assessment of road scenes using Inevitable Collision States

This paper presents a method for reasoning about the safety of traffic situations. More precisely, the problem of safety assessment for partial trajectories for vehicles is addressed. Therefore, the Inevitable Collision States (ICS) as well as its probabilistic generalization the Probabilistic Collision States (PCS) are used. Thereby, the assessment is performed for an infinite time horizon. For solving the ICS computation nonlinear programming is applied. In addition to the safety assessment an evaluation of the disturbance of the other traffic participants by the ego vehicle is presented. The results are integrated into an optimal control based planning approach that generates minimum jerk trajectories. An example implementation of the proposed framework is applied to simulation scenarios that demonstrates the necessity of the presented method for guaranteeing motion safety.

Track-to-track fusion with asynchronous sensors and out-of-sequence tracks using information matrix fusion for advanced driver assistance systems

Future advanced driver assistance systems will contain multiple sensors that are used for several applications, such as highly automated driving on freeways. The problem is that the sensors are usually asynchronous and their data possibly out-of-sequence, making fusion of the sensor data non-trivial. This paper presents a novel approach to track-to-track fusion for automotive applications with asynchronous and out-of-sequence sensors using information matrix fusion. This approach solves the problem of correlation between sensor data due to the common process noise and common track history, which eliminates the need to replace the global track estimate with the fused local estimate at each fusion cycle. The information matrix fusion approach is evaluated in simulation and its performance demonstrated using real sensor data on a test vehicle designed for highly automated driving on freeways.

Strategic Decision-Making Process in Advanced Driver Assistance Systems

Abstract Contemporary driver assistance systems obtain a steadily increasing grade of automation. A novel system, the emergency stop assistant, performs completely autonomous driving in emergency situations. This system takes over the vehicles control, steers the vehicle safely to the roadside and stops, if the driver suffers a health irregularity such as a heart attack. In order to navigate through the traffic, sensors have to gain information, concerning the road and the surrounding vehicles. Subsequently, the gathered data is used to analyze and interpret the traffic situation. Depending on the current situation, driving strategies are derived, which navigate the vehicle autonomously to the roadside. The information concerning suitable areas to stop the vehicle is obtained from a digital map. This paper presents a novel method of situation interpretation based on static and dynamic state space assessment. Furthermore, the decision-making process in driving strategies using a combination of hybrid automata and decision trees will be presented.