4,920 research outputs found

    Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme

    Full text link
    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

    Get PDF
    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

    Get PDF
    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

    Full text link
    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 nn 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 O(nlogn)O(n \log n) 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
    corecore