G. Farber

Bio: G. Farber is an academic researcher from Technische Universität München. The author has contributed to research in topics: Advanced driver assistance systems & Traffic simulation. The author has an hindex of 3, co-authored 3 publications receiving 191 citations.

Cooperative Cognitive Automobiles

Abstract: Safety requirements are among the most ambitious challenges for autonomous guidance and control of automobiles. A human-like understanding of the surrounding traffic scene is a key element to fulfill these requirements, but is a still missing capability of today's intelligent vehicles. Few recent proposals for driver assistance systems approach this issue with methods from the AI research to allow for a reasonable situation evaluation and behavior generation. While the methods proposed in this contribution are lend from cognition in order to mimic human capabilities, we argue that in the long term automated cooperation among traffic participants bears the potential to improve traffic efficiency and safety beyond the level attainable by human drivers. Both issues are major objectives of the Transregional Collaborative Research Centre 28 'cognitive automobiles,' TCRC28 that is outlined in the paper. Within this project the partners focus on systematic and interdisciplinary research on machine cognition of mobile systems as the basis for a scientific theory of automated machine behavior.

Validation of the Vehicle in the Loop (VIL); A milestone for the simulation of driver assistance systems

Abstract: The vehicle in the loop test setup has been developed for the safe, reproducible and resources-saving test of driver assistance functions for support in critical traffic situations. This setup combines the advantages of driving simulators and a real test vehicle by incorporating it into a traffic simulation. While driving, the synthetic outside traffic is visualized to the driver realistically by means of an optical see through head mounted display. Thanks to the vehicle in the loop test setup, motion sickness is avoided. With the help of sensor models, driver assistance functions can react to synthetic outside traffic already in an early phase of development, and the function can thus be tested realistically and without danger for humans and machine.

Vehicle in the Loop (VIL) – A New Simulator Set-Up for Testing Advanced Driving Assistance Systems

Abstract: This paper describes how the vehicle in the loop test setup has been developed for the safe, reproducible and resource-saving test of driver assistance functions for support in critical traffic situations This setup combines the advantages of driving simulators and a real test vehicle by incorporating it into a traffic simulation While driving, the synthetic outside traffic is visualized to the driver realistically by means of an optical see through Head Mounted Display Thanks to the Vehicle in the Loop test setup, motion sickness is avoided With the help of sensor models, driver assistance functions can react to synthetic outside traffic already in an early phase of development, and the function can thus be tested realistically and without danger for humans and machine

Three Decades of Driver Assistance Systems: Review and Future Perspectives

Abstract: This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.

Computer-aided system for 360º heads up display of safety/mission critical data

Abstract: A safety critical, time sensitive data system for projecting safety/mission critical data onto a display pair of Commercial Off The Shelf (COTS) light weight projection glasses or monocular creating an immersive omnidirectional HUD (Heads Up Display) system with 6 degrees of freedom movement with user immersion and user inputs & outputs. The system includes the display, haptic glove, haptic suit, vestibular interface, temperature emulation, smell emulation, and omnidirectional sound, the workstation, the application software, and inputs containing the safety/mission critical information (Current User Position, Total Collision Avoidance System—TCAS, Global Positioning System—GPS, Magnetic Resonance Imaging—MRI Images, CAT scan images. Weather data. Military troop data, real-time space type markings etc.). The workstation software processes the incoming safety/mission critical data and converts it into a three dimensional space for the user to immerse into the environment. Selecting any of the images may display available information about the selected item or may enhance the image, or moving hand or body will generate enhanced environmental perception & awareness, as well as inputs and outputs. Predicted position vectors may be displayed as well as 3D terrain.

Model-Based Probabilistic Collision Detection in Autonomous Driving

Abstract: The safety of the planned paths of autonomous cars with respect to the movement of other traffic participants is considered. Therefore, the stochastic occupancy of the road by other vehicles is predicted. The prediction considers uncertainties originating from the measurements and the possible behaviors of other traffic participants. In addition, the interaction of traffic participants, as well as the limitation of driving maneuvers due to the road geometry, is considered. The result of the presented approach is the probability of a crash for a specific trajectory of the autonomous car. The presented approach is efficient as most of the intensive computations are performed offline, which results in a lean online algorithm for real-time application.

A systematic review of perception system and simulators for autonomous vehicles research

Abstract: This paper presents a systematic review of the perception systems and simulators for autonomous vehicles (AV). This work has been divided into three parts. In the first part, perception systems are categorized as environment perception systems and positioning estimation systems. The paper presents the physical fundamentals, principle functioning, and electromagnetic spectrum used to operate the most common sensors used in perception systems (ultrasonic, RADAR, LiDAR, cameras, IMU, GNSS, RTK, etc.). Furthermore, their strengths and weaknesses are shown, and the quantification of their features using spider charts will allow proper selection of different sensors depending on 11 features. In the second part, the main elements to be taken into account in the simulation of a perception system of an AV are presented. For this purpose, the paper describes simulators for model-based development, the main game engines that can be used for simulation, simulators from the robotics field, and lastly simulators used specifically for AV. Finally, the current state of regulations that are being applied in different countries around the world on issues concerning the implementation of autonomous vehicles is presented.

Reachability Analysis and its Application to the Safety Assessment of Autonomous Cars

Abstract: This thesis is about the safety verification of dynamical systems using reachability analysis. Novel solutions have been developed for classical reachability analysis, stochastic reachability analysis, and their application to the safety assessment of autonomous cars. Classical reachability analysis computes the set of states that can be reached by a system. If the reachable set does not intersect any set of unsafe states, the safety of the system is guaranteed. Algorithms for this problem have been developed for linear, nonlinear, and hybrid systems. Stochastic reachability analysis measures the probability of reaching an unsafe set. One pursued approach computes over-approximative solutions for linear systems; another one generates a Markov chain which approximately computes the stochastic reachable set of arbitrary dynamics.