4,920 research outputs found
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
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
Adaptive routing of autonomous vehicles using neighborhood traffic
As research on autonomous vehicles increases, automotive manufacturers and researchers are developing coordination techniques to enable safe passage of vehicles through intersections. These techniques are called Autonomous Intersection Management (AIM). Even though AIM techniques improve intersection throughput, they do not effectively reduce congestion. Real life urban roads comprise of a networks of multiple intersections. In such scenarios, communicating traffic information between intersections is essential for reducing congestion on the roads. To achieve this, we propose an adaptive routing algorithm that incorporates a fusion of vehicle-to-intersection (V2I) communication and intersection-to-intersection (I2I) communication in order to bring about significant reductions in congestion. To implement this algorithm, we constructed the Enhanced AIM simulation framework as an extension of AIM simulator (University of Texas, Austin). We demonstrate with simulation experiments that our proposed routing algorithm shows reduced congestion and wait-time, and improved user experience.autonomous vehiclesAutonomous Intersection Management (AIM)urban roadsneighborhood traffi
On the Complexity of an Unregulated Traffic Crossing
The steady development of motor vehicle technology will enable cars of the
near future to assume an ever increasing role in the decision making and
control of the vehicle itself. In the foreseeable future, cars will have the
ability to communicate with one another in order to better coordinate their
motion. This motivates a number of interesting algorithmic problems. One of the
most challenging aspects of traffic coordination involves traffic
intersections. In this paper we consider two formulations of a simple and
fundamental geometric optimization problem involving coordinating the motion of
vehicles through an intersection.
We are given a set of vehicles in the plane, each modeled as a unit
length line segment that moves monotonically, either horizontally or
vertically, subject to a maximum speed limit. Each vehicle is described by a
start and goal position and a start time and deadline. The question is whether,
subject to the speed limit, there exists a collision-free motion plan so that
each vehicle travels from its start position to its goal position prior to its
deadline.
We present three results. We begin by showing that this problem is
NP-complete with a reduction from 3-SAT. Second, we consider a constrained
version in which cars traveling horizontally can alter their speeds while cars
traveling vertically cannot. We present a simple algorithm that solves this
problem in time. Finally, we provide a solution to the discrete
version of the problem and prove its asymptotic optimality in terms of the
maximum delay of a vehicle
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