137 research outputs found
Optimal Behavior Planning for Autonomous Driving: A Generic Mixed-Integer Formulation
Mixed-Integer Quadratic Programming (MIQP) has been identified as a suitable
approach for finding an optimal solution to the behavior planning problem with
low runtimes. Logical constraints and continuous equations are optimized
alongside. However, it has only been formulated for a straight road, omitting
common situations such as taking turns at intersections. This has prevented the
model from being used in reality so far. Based on a triple integrator model
formulation, we compute the orientation of the vehicle and model it in a
disjunctive manner. That allows us to formulate linear constraints to account
for the non-holonomy and collision avoidance. These constraints are
approximations, for which we introduce the theory. We show the applicability in
two benchmark scenarios and prove the feasibility by solving the same models
using nonlinear optimization. This new model will allow researchers to leverage
the benefits of MIQP, such as logical constraints, or global optimality.Comment: Published at IEEE Intelligent Vehicles Symposium (IV), 202
Interaction-Aware Motion Planning for Automated Vehicles
Die Bewegungsplanung für automatisierte Fahrzeuge (AVs) in gemischtem Verkehr ist eine herausfordernde Aufgabe. Hierbei bezeichnet gemischter Verkehr, Verkehr bestehend aus von Menschen gefahrenen Fahrzeugen sowie automatisierten Fahrzeugen. Um die Komplexität der Aufgabe zu reduzieren, verwenden state-of-the-art Planungsansätze oft die vereinfachende Annahme, dass das zukünftige Verhalten umliegender Fahrzeuge unabhängig vom Plan des AVs vorhergesagt werden kann. Während die Trennung von Prädiktion und Planung für viele Verkehrssituationen eine hilfreiche Vereinfachung darstellt, werden hierbei Interaktionen zwischen den Verkehrsteilnehmern ignoriert, was besonders in interaktiven Verkehrssituationen zu suboptimalem, übermäßig konservativem Fahrverhalten führen kann.
In dieser Arbeit werden zwei interaktionsbewusste Bewegungsplanungsalgorithmen vorgeschlagen, die in der Lage sind übermäßig konservatives Fahrverhalten zu reduzieren. Der Kernaspekt dieser Algorithmen ist, dass Prädiktion und Planung gleichzeitig gelöst werden. Mit diesen Algorithmen können anspruchsvolle Fahrmanöver, wie z. B. das Reißverschlussverfahren in dichtem Verkehr, durchgeführt werden, die mit state-of-the-art Planungsansätzen nicht möglich sind.
Der erste Algorithmus basiert auf Methoden der Multi-Agenten-Planung. Interaktionen zwischen Verkehrsteilnehmern werden durch Optimierung gekoppelter Trajektorien mittels einer gemeinsamen Kostenfunktion approximiert. Das Kernstück des Algorithmus ist eine neuartige Multi-Agenten-Trajektorienplanungsformulierung, die auf gemischt-ganzzahliger quadratischer Programmierung (MIQP) basiert. Die Formulierung garantiert global optimale Lösungen und ist somit in der Lage das kombinatorische Problem zu lösen, welches kontinuierliche Methoden auf lokal optimale Lösungen beschränkt. Desweiteren kann durch den vorgestellten Ansatz ein manöverneutrales Verhalten erzeugt werden, das Manöverentscheidungen in ungewissen Situationen aufschieben kann.
Der zweite Ansatz formuliert Interaktionen zwischen einem menschlichen Fahrer und einem AV als ein Stackelberg-Spiel. Im Gegensatz zu bestehenden Arbeiten kann der Algorithmus allgemeine nichtlineare Zustands- und Eingabebeschränkungen berücksichtigen. Desweiteren führen wir Mechanismen zur Integration von Kooperation und Rücksichtnahme in die Planung ein. Damit wird übermäßig aggressives Fahrverhalten verhindert, was in der Literatur als ein Problem interaktionsbewusster Planungsmethoden identifiziert wurde. Die Wirksamkeit, Robustheit und Echtzeitfähigkeit des Algorithmus wird durch numerische Experimente gezeigt
An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning
Mobile robotic platforms are an indispensable tool for various scientific and
industrial applications. Robots are used to undertake missions whose execution
is constrained by various factors, such as the allocated time or their
remaining energy. Existing solutions for resource constrained multi-robot
sensing mission planning provide optimal plans at a prohibitive computational
complexity for online application [1],[2],[3]. A heuristic approach exists for
an online, resource constrained sensing mission planning for a single vehicle
[4]. This work proposes a Genetic Algorithm (GA) based heuristic for the
Correlated Team Orienteering Problem (CTOP) that is used for planning sensing
and monitoring missions for robotic teams that operate under resource
constraints. The heuristic is compared against optimal Mixed Integer Quadratic
Programming (MIQP) solutions. Results show that the quality of the heuristic
solution is at the worst case equal to the 5% optimal solution. The heuristic
solution proves to be at least 300 times more time efficient in the worst
tested case. The GA heuristic execution required in the worst case less than a
second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and
Automation Letters (RA-L
Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme
Behavior planning and decision-making are some of the biggest challenges for
highly automated systems. A fully automated vehicle (AV) is confronted with
numerous tactical and strategical choices. Most state-of-the-art AV platforms
implement tactical and strategical behavior generation using finite state
machines. However, these usually result in poor explainability, maintainability
and scalability. Research in robotics has raised many architectures to mitigate
these problems, most interestingly behavior-based systems and hybrid
derivatives. Inspired by these approaches, we propose a hierarchical
behavior-based architecture for tactical and strategical behavior generation in
automated driving. It is a generalizing and scalable decision-making framework,
utilizing modular behavior blocks to compose more complex behaviors in a
bottom-up approach. The system is capable of combining a variety of scenario-
and methodology-specific solutions, like POMDPs, RRT* or learning-based
behavior, into one understandable and traceable architecture. We extend the
hierarchical behavior-based arbitration concept to address scenarios where
multiple behavior options are applicable but have no clear priority against
each other. Then, we formulate the behavior generation stack for automated
driving in urban and highway environments, incorporating parking and emergency
behaviors as well. Finally, we illustrate our design in an explanatory
evaluation
A Computationally Efficient Bi-level Coordination Framework for CAVs at Unsignalized Intersections
In this paper, we investigate cooperative vehicle coordination for connected
and automated vehicles (CAVs) at unsignalized intersections. To support high
traffic throughput while reducing computational complexity, we present a novel
collision region model and decompose the optimal coordination problem into two
sub-problems: \textit{centralized} priority scheduling and \textit{distributed}
trajectory planning. Then, we propose a bi-level coordination framework which
includes: (i) a Monte Carlo Tree Search (MCTS)-based high-level priority
scheduler aims to find high-quality passing orders to maximize traffic
throughput, and (ii) a priority-based low-level trajectory planner that
generates optimal collision-free control inputs. Simulation results demonstrate
that our bi-level strategy achieves near-optimal coordination performance,
comparable to state-of-the-art centralized strategies, and significantly
outperform the traffic signal control systems in terms of traffic throughput.
Moreover, our approach exhibits good scalability, with computational complexity
scaling linearly with the number of vehicles. Video demonstrations can be found
online at \url{https://youtu.be/WYAKFMNnQfs}
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning
Recent advances in combining deep learning and Reinforcement Learning have
shown a promising path for designing new control agents that can learn optimal
policies for challenging control tasks. These new methods address the main
limitations of conventional Reinforcement Learning methods such as customized
feature engineering and small action/state space dimension requirements. In
this paper, we leverage one of the state-of-the-art Reinforcement Learning
methods, known as Trust Region Policy Optimization, to tackle intersection
management for autonomous vehicles. We show that using this method, we can
perform fine-grained acceleration control of autonomous vehicles in a grid
street plan to achieve a global design objective.Comment: Accepted in IEEE Smart World Congress 201
Optimisation-based coordination of connected, automated vehicles at intersections
In this paper, we analyse the performance of a model predictive controller for coordination of connected, automated vehicles at intersections. The problem has combinatorial complexity, and we propose to solve it approximately by using a two stage procedure where (1) the vehicle crossing order in which the vehicles cross the intersection is found by solving a mixed integer quadratic program and (2) the control commands are subsequently found by solving a nonlinear program. We show that the controller is persistently safe and compare its performance against traffic lights and two simpler optimisation-based coordination schemes. The results show that our approach outperforms the considered alternatives in terms of both energy consumption and travel-time delay, especially for medium to high traffic loads
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