Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search

Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency. Observing the behavior of others, humans infer whether or not others are cooperating. This work aims to extend the capabilities of automated vehicles, enabling them to cooperate implicitly in heterogeneous environments. Continuous actions allow for arbitrary trajectories and hence are applicable to a much wider class of problems than existing cooperative approaches with discrete action spaces. Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS) in conjunction with Decoupled-UCT evaluates the action-values of each agent in a cooperative and decentralized way, respecting the interdependence of actions among traffic participants. The extension to continuous action spaces is addressed by incorporating novel MCTS-specific enhancements for efficient search space exploration. The proposed algorithm is evaluated under different scenarios, showing that the algorithm is able to achieve effective cooperative planning and generate solutions egocentric planning fails to identify

Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search

Today's automated vehicles lack the ability to cooperate implicitly with others. This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments. Based on cooperative modeling of other agents and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the state-action-values of each agent in a cooperative and decentralized manner, explicitly modeling the interdependence of actions between traffic participants. Macro-actions allow for temporal extension over multiple time steps and increase the effective search depth requiring fewer iterations to plan over longer horizons. Without predefined policies for macro-actions, the algorithm simultaneously learns policies over and within macro-actions. The proposed method is evaluated under several conflict scenarios, showing that the algorithm can achieve effective cooperative planning with learned macro-actions in heterogeneous environments

Multiple Waypoint Navigation in Unknown Indoor Environments

Indoor motion planning focuses on solving the problem of navigating an agent through a cluttered environment. To date, quite a lot of work has been done in this field, but these methods often fail to find the optimal balance between computationally inexpensive online path planning, and optimality of the path. Along with this, these works often prove optimality for single-start single-goal worlds. To address these challenges, we present a multiple waypoint path planner and controller stack for navigation in unknown indoor environments where waypoints include the goal along with the intermediary points that the robot must traverse before reaching the goal. Our approach makes use of a global planner (to find the next best waypoint at any instant), a local planner (to plan the path to a specific waypoint), and an adaptive Model Predictive Control strategy (for robust system control and faster maneuvers). We evaluate our algorithm on a set of randomly generated obstacle maps, intermediate waypoints, and start-goal pairs, with results indicating a significant reduction in computational costs, with high accuracies and robust control.Comment: Accepted at ICCR 202

Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios

Abstract-We present a method for motion planning in the presence of moving obstacles that is aimed at dynamic on-road driving scenarios. Planning is performed within a geometric graph that is established by sampling deterministically from a manifold that is obtained by combining configuration space and time. We show that these graphs are acyclic and shortest path algorithms with linear runtime can be employed. By reparametrising the configuration space to match the course of the road, it can be sampled very economically with few vertices, and this reduces absolute runtime further. The trajectories generated are quintic splines. They are second order continuous, obey nonholonomic constraints and are optimised for minimum square of jerk. Planning time remains below 20 ms on general purpose hardware

Software architectural design for safety in Automated Parking System

The automotive industry has seen a revolution brought about by self-driving cars. However, one of the main challenges facing autonomous driving systems is ensuring safety in the absence of a supervising driver and verifying safe vehicle behaviour under various circumstances. Autonomous Driving Systems (ADS), due to their complexity, cannot be solved straightforwardly without proper structure. Thus, they need a well-defined architecture to guide their development with requirements that involve modularity, scalability, and maintainability among other properties. To help overcome some of the challenges, this master thesis defines and implements in a simulated environment an automated parking system that complies with industrial and safety standards. The work has been divided into four parts. Firstly, the safety rules for the development of an autonomous function have been analysed. Secondly, the use cases and system requirements have been defined following the needs of the automated parking system. Thirdly, the system has been implemented in the simulation environment with a structure based on a widely adopted automotive standard. The final result is the software architecture of an autonomous vehicle with automated parking functionality. This concept has been validated within the virtual environment together with the integration of the AUTOSAR runtime environment, which the communication between components and mode switching functionality in the CARLA simulation environment. The result of this project shows the benefit of integrating architecture and simulation, thus easing the development and testing of future autonomous systems

Optimale Bahn- und Trajektorienplanung für Automobile

In dieser Arbeit werden verschiedene Ansätze für die automatische Trajektorienplanung eines vorderradgelenkten Fahrzeuges erarbeitet. Es werden dabei sowohl globale, kombinatorische Prinzipien untersucht als auch lokale, kontinuierliche. Es wird über umfangreichen Fahrversuche berichtet, mit denen die entwickelten Verfahren erprobt wurden

Probabilistic Motion Planning for Automated Vehicles

This thesis targets the problem of motion planning for automated vehicles. As a prerequisite for their on-road deployment, automated vehicles must show an appropriate and reliable driving behavior in mixed traffic, i.e. alongside human drivers. Besides the uncertainties resulting from imperfect perception, occlusions and limited sensor range, also the uncertainties in the behavior of other traffic participants have to be considered. Related approaches for motion planning in mixed traffic often employ a deterministic problem formulation. The solution of such formulations is restricted to a single trajectory. Deviations from the prediction of other traffic participants are accounted for during replanning, while large uncertainties lead to conservative and over-cautious behavior. As a result of the shortcomings of these formulations in cooperative scenarios and scenarios with severe uncertainties, probabilistic approaches are pursued. Due to the need for real-time capability, however, a holistic uncertainty treatment often induces a strong limitation of the action space of automated vehicles. Moreover, safety and traffic rule compliance are often not considered. Thus, in this work, three motion planning approaches and a scenario-based safety approach are presented. The safety approach is based on an existing concept, which targets the guarantee that automated vehicles will never cause accidents. This concept is enhanced by the consideration of traffic rules for crossing and merging traffic, occlusions, limited sensor range and lane changes. The three presented motion planning approaches are targeted towards the different predominant uncertainties in different scenarios, while operating in a continuous action space. For non-interactive scenarios with clear precedence, a probabilistic approach is presented. The problem is modeled as a partially observable Markov decision process (POMDP). In contrast to existing approaches, the underlying assumption is that the prediction of the future progression of the uncertainty in the behavior of other traffic participants can be performed independently of the automated vehicle\u27s motion plan. In addition to this prediction of currently visible traffic participants, the influence of occlusions and limited sensor range is considered. Despite its thorough uncertainty consideration, the presented approach facilitates planning in a continuous action space. Two further approaches are targeted towards the predominant uncertainties in interactive scenarios. In order to facilitate lane changes in dense traffic, a rule-based approach is proposed. The latter seeks to actively reduce the uncertainty in whether other vehicles willingly make room for a lane change. The generated trajectories are safe and traffic rule compliant with respect to the presented safety approach. To facilitate cooperation in scenarios without clear precedence, a multi-agent approach is presented. The globally optimal solution to the multi-agent problem is first analyzed regarding its ambiguity. If an unambiguous, cooperative solution is found, it is pursued. Still, the compliance of other vehicles with the presumed cooperation model is checked, and a conservative fallback trajectory is pursued in case of non-compliance. The performance of the presented approaches is shown in various scenarios with intersecting lanes, partly with limited visibility, as well as lane changes and a narrowing without predefined right of way

Trajectory Planning for Collision Avoidance in Urban Scenarios

In dieser Arbeit werden Ansätze zur Trajektorienplanung vorgestellt, die ein automatisiertes Ausweichen mit einem einzelnen kritischen dynamischen Hindernis in beschränkter Umgebung ermöglichten. Hierzu werden drei Varianten vorgestellt, die eine Kopplung von Längs- und Querdynamik erlauben: Die erste Variante betrachtet die Problemstellung der optimale Trajektorienplanung unter Berücksichtigung eines dynamischen Fahrzeugmodells. Die zweite Variante soll durch eine geeignete Parametrierung der Trajektorien das Verhalten des Fahrzeugmodells approximieren. Die dritte Variante erweitert die Zweite durch einen heuristischen Ansatz zur zeitlichen Abtastung. Das Potenzial der drei vorgestellten Verfahren wird im ersten Schritt simulativ nachgewiesen und im darauffolgenden Schritt wird das dritte Verfahren in einem Versuchsträger zum Nachweis der Echtzeitfähigkeit implementiert. Es wird gezeigt, dass die Ansätze für die Anwendung in Fahrerassistenzsystemen geeignet sind

Ein neues Konzept für die Trajektoriengenerierung und -stabilisierung in zeitkritischen Verkehrsszenarien

Durch den Einsatz autonomer Fahrzeuge kann der Straßenverkehr effizienter, komfortabler und vor allem sicherer gestaltet werden. Neben der hierfür erforderlichen Umfeldwahrnehmung stellen besonders die Bewegungsplanung und -ausführung zeitkritischer Fahrmanöver zur Beherrschung von dynamischen Verkehrsszenarien eine große Herausforderung dar. Herkömmliche Verfahren, die trotz trickreicher Modifikationen dieser nicht gewachsen sind, werden konsequent durch trajektorienbasierte Konzepte ersetzt

Ein neues Konzept für die Trajektoriengenerierung und -stabilisierung in zeitkritischen Verkehrsszenarien

Durch den Einsatz autonomer Fahrzeuge kann der Straßenverkehr effizienter, komfortabler und vor allem sicherer gestaltet werden. Neben der hierfür erforderlichen Umfeldwahrnehmung stellen besonders die Bewegungsplanung und -ausführung zeitkritischer Fahrmanöver zur Beherrschung von dynamischen Verkehrsszenarien eine große Herausforderung dar. Herkömmliche Verfahren, die trotz trickreicher Modifikationen dieser nicht gewachsen sind, werden konsequent durch trajektorienbasierte Konzepte ersetzt