Benjamin Gutjahr

Bio: Benjamin Gutjahr is an academic researcher from BMW. The author has contributed to research in topics: Collision avoidance & Collision. The author has an hindex of 5, co-authored 10 publications receiving 174 citations.

Lateral Vehicle Trajectory Optimization Using Constrained Linear Time-Varying MPC

Abstract: In this paper, a trajectory optimization algorithm is proposed, which formulates the lateral vehicle guidance task along a reference curve as a constrained optimal control problem. The optimization problem is solved by means of a linear time-varying model predictive control scheme that generates trajectories for path following under consideration of various time-varying system constraints in a receding horizon fashion. Formulating the system dynamics linearly in combination with a quadratic cost function has two great advantages. First, the system constraints can be set up not only to achieve collision avoidance with both static and dynamic obstacles, but also aspects of human driving behavior can be considered. Second, the optimization problem can be solved very efficiently, such that the algorithm can be run with little computational effort. In addition, due to an elaborate problem formulation, reference curves with discontinuous, high curvatures will be effortlessly smoothed out by the algorithm. This makes the proposed algorithm applicable to different traffic scenarios, such as parking or highway driving. Experimental results are presented for different real-world scenarios to demonstrate the algorithm’s abilities.

Dynamische Neuplanung einer Fahrtrajektorie mittels LQ-Regelung für einen Ausweichassistenten

Abstract: Die Erfindung betrifft eine Vorrichtung und ein Verfahren zur dynamischen Planung von Fahrtrajektorien, insbesondere zur Bereitstellung eines Ausweichassistenten fur ein Fahrzeug. Es wird ein Verfahren zur Ermittlung einer Ausweichtrajektorie eines Fahrzeugs beschrieben. Die Ausweichtrajektorie wird in Bezug auf ein Hindernis ermittelt, das in eine pradizierte Fahrzeugtrajektorie eingreift. Das Verfahren umfasst das Ermitteln einer Bewegung des Fahrzeugs. Weiter umfasst das Verfahren das Ermitteln einer Position des Hindernisses. Daruber hinaus umfasst das Verfahren das Transformieren der Bewegung des Fahrzeugs und der Position des Hindernisses in Zustandsdaten relativ zu einer Referenztrajektorie. Dabei entspricht die Referenztrajektorie einer begradigten Version der pradizierten Fahrzeugtrajektorie. Auserdem umfasst das Verfahren das Bestimmen einer Stellgrose zur Beeinflussung der Bewegung des Fahrzeugs auf Basis der Zustandsdaten, wobei die Stellgrose eine Bewegung des Fahrzeugs entlang einer Ausweichtrajektorie.

Automatic collision avoidance during parking and maneuvering — An optimal control approach

Abstract: In order to reduce the great number of parking incidences and other collisions in low speed scenarios, an obstacle avoidance algorithm is proposed. Since collision avoidance can be achieved by sole braking when driving slowly this algorithm's objective is a comfort orientated braking routine. Therefore, an optimization problem is formulated leading to jerk and time optimal trajectories for braking. In addition to that, based on the prediction of the future vehicle motion, this algorithm compensates for a significant actuator time delay. Using an occupancy grid for representing the static vehicle environment and the current driving state, possible collision points are determined not only for the vehicle front or rear, but also for both vehicle sides, where no sensors are located. The algorithm's performance is demonstrated in a real-world scenario.

Recheneffiziente Trajektorienoptimierung für Fahrzeuge mittels quadratischem Programm

Abstract: Zusammenfassung Die aus der Literatur bekannten Verfahren zur Optimierung von Fahrtrajektorien überfordern kurz und mittelfristig die Fahrzeugsteuergeräte hinsichtlich ihrer Rechenleistung. Um auch neu aufkommenden Qualitätsanforderungen zu entsprechen, nutzt die vorliegende Arbeit die aus der Literatur bekannten Vorteile der beschränkten, linear-quadratischen Optimierung zur recheneffizienten Lösungsfindung gewinnbringend aus. Hierbei führt die ausgewählte Problemformulierung basierend auf dem anwendungsspezifischem Einsatz von Schlupfvariablen zu funktionalen Weiterentwicklungen, sodass neben der priorisierten Kollisionsvermeidung auch Aspekte natürlichen Fahrverhaltens auf besondere Weise berücksichtigt werden können. Die Erprobung der umgesetzten Fahrzeuglängs- und -querführung im Realversuch verdeutlicht das große Potential dieses Ansatzes für automatisierte Fahrfunktionen.

Verfahren zur Ermittlung einer Fahrzeug-Trajektorie entlang einer Referenzkurve

Abstract: Es wird ein Verfahren zur Ermittlung einer Trajektorie zur Fuhrung eines Fahrzeugs beschrieben Das Verfahren umfasst das Ermitteln einer Fuhrungs-Referenzkurve, entlang der das Fahrzeug gefuhrt werden soll Das Verfahren umfasst weiter das Ermitteln einer Linearisierungs-Referenzkurve, so dass die Linearisierungs-Referenzkurve hohere Stetigkeitsanforderungen erfullt als die Fuhrungs-Referenzkurve Auserdem umfasst das Verfahren das Ermitteln von Anfangswerten fur eine Mehrzahl von Zustandsgrosen des Fahrzeugs relativ zu der Linearisierungs-Referenzkurve Desweiteren umfasst das Verfahren das Bestimmen einer Trajektorie auf Basis der Anfangswerte, auf Basis der Fuhrungs-Referenzkurve und auf Basis eines Modells der Dynamik des Fahrzeugs Dabei umfasst das Bestimmen der Trajektorie das Ermitteln einer Gutefunktion, die eine Abweichung der Trajektorie von der Fuhrungs-Referenzkurve berucksichtigt

Lateral Vehicle Trajectory Optimization Using Constrained Linear Time-Varying MPC

Abstract: In this paper, a trajectory optimization algorithm is proposed, which formulates the lateral vehicle guidance task along a reference curve as a constrained optimal control problem. The optimization problem is solved by means of a linear time-varying model predictive control scheme that generates trajectories for path following under consideration of various time-varying system constraints in a receding horizon fashion. Formulating the system dynamics linearly in combination with a quadratic cost function has two great advantages. First, the system constraints can be set up not only to achieve collision avoidance with both static and dynamic obstacles, but also aspects of human driving behavior can be considered. Second, the optimization problem can be solved very efficiently, such that the algorithm can be run with little computational effort. In addition, due to an elaborate problem formulation, reference curves with discontinuous, high curvatures will be effortlessly smoothed out by the algorithm. This makes the proposed algorithm applicable to different traffic scenarios, such as parking or highway driving. Experimental results are presented for different real-world scenarios to demonstrate the algorithm’s abilities.

Vehicles of the Future: A Survey of Research on Safety Issues

Abstract: Information and communication technologies (ICTs) have a profound impact on the current state and envisioned future of automobiles. This paper presents an overview of research on ICT-based support and assistance services for the safety of future connected vehicles. A general classification and a brief description of the focus areas for research and development in this direction are given under the titles of vehicle detection, road detection, lane detection, pedestrian detection, drowsiness detection, and collision avoidance. Following an overview and taxonomy of the reviewed research articles, a categorized literature survey of safety critical applications is presented in detail. Future research directions are also highlighted.

Cooperative Trajectory Planning for Haptic Shared Control Between Driver and Automation in Highway Driving

Abstract: This paper addresses the driver–automation shared driving control for lane keeping and obstacle avoidance of automated vehicles in highway traffic. The proposed shared control framework is established from a novel cooperative trajectory planning algorithm and a fuzzy steering controller. Based on polynomial functions, the cooperative trajectory planning is formulated by judiciously exploiting the information on the maneuver decision, the conflict management, and the driver monitoring. As a result, the planned trajectory of the vehicle is continuously adapted according to the driver's actions and intentions. By means of Lyapunov stability arguments, sufficient conditions in terms of linear matrix inequalities are given to design a Takagi–Sugeno fuzzy model-based controller. This robust steering controller provides a necessary assistive torque to track the planned vehicle trajectory. The new shared driving control framework allows reducing effectively the driver–automation conflict issue while offering the driver more freedom to swerve within a predefined lane. The advantages of the proposed approach are evaluated using both objective and subjective results, experimentally obtained from several human drivers and an advanced interactive dynamic driving simulator.

Collision Avoidance: A Literature Review on Threat-Assessment Techniques

Abstract: For the last few decades, a lot of attention has been given to intelligent vehicle systems, and in particular to automated safety and collision avoidance solutions. In this paper, we present a literature review and analysis of threat-assessment methods used for collision avoidance. We will cover algorithms that are based on single-behavior threat metrics, optimization methods, formal methods, probabilistic frameworks, and data driven approaches, i.e., machine learning. The different theoretical algorithms are finally discussed in terms of computational complexity, robustness, and most suited applications.

A Novel Dual Successive Projection-Based Model-Free Adaptive Control Method and Application to an Autonomous Car

Abstract: In this paper, a novel model-free adaptive control (MFAC) algorithm based on a dual successive projection (DuSP)-MFAC method is proposed, and it is analyzed using the introduced DuSP method and the symmetrically similar structures of the controller and its parameter estimator of MFAC. Then, the proposed DuSP-MFAC scheme is successfully implemented in an autonomous car “Ruilong” for the lateral tracking control problem via converting the trajectory tracking problem into a stabilization problem by using the proposed preview-deviation-yaw angle. This MFAC-based lateral tracking control method was tested and demonstrated satisfactory performance on real roads in Fengtai, Beijing, China, and through successful participation in the Chinese Smart Car Future Challenge Competition held in 2015 and 2016.