393 research outputs found
A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
Vehicle to Vehicle (V2V) communication has a great potential to improve
reaction accuracy of different driver assistance systems in critical driving
situations. Cooperative Adaptive Cruise Control (CACC), which is an automated
application, provides drivers with extra benefits such as traffic throughput
maximization and collision avoidance. CACC systems must be designed in a way
that are sufficiently robust against all special maneuvers such as cutting-into
the CACC platoons by interfering vehicles or hard braking by leading cars. To
address this problem, a Neural- Network (NN)-based cut-in detection and
trajectory prediction scheme is proposed in the first part of this paper. Next,
a probabilistic framework is developed in which the cut-in probability is
calculated based on the output of the mentioned cut-in prediction block.
Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed
which incorporates this cut-in probability to enhance its reaction against the
detected dangerous cut-in maneuver. The overall system is implemented and its
performance is evaluated using realistic driving scenarios from Safety Pilot
Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I
A Supervisor Αgent-Based on the Markovian Decision Process Framework to Optimize the Behavior of a Highly Automated System
In this paper, we explore how MDP can be used as the framework to design and develop an Intelligent Decision Support System/Recommender System, in order to extend human perception and overcome human senses limitations (because covered by the ADS), by augmenting human cognition, emphasizing human judgement and intuition, as well as supporting him/her to take the proper decision in the right terms and time. Moreover, we develop Human-Machine Interaction (HMI) strategies able to make “transparent” the decision-making/recommendation process. This is strongly needed, since the adoption of partial automated systems is not only connected to the effectiveness of the decision and control processes, but also relies on how these processes are communicated and “explained” to the human driver, in order to achieve his/her trust
Predictive cruise control with autonomous overtaking
This paper studies the problem of optimally controlling an autonomous vehicle, to safely overtake a slow-moving leading vehicle. The problem is formulated to minimize deviation from a reference velocity and position trajectory, while keeping the vehicle on the road and avoiding collision with surrounding vehicles. We show that the optimization problem can be formulated as a convex program, by providing convex modeling steps that include change of reference frame, change of variables, sampling in relative longitudinal distance, convex relaxation and linearization. A case study is provided showing overtaking scenarios in proximity of an oncoming vehicle, and a vehicle driving on an adjacent lane and in the same direction as the leading vehicle
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning
For safe and efficient planning and control in autonomous driving, we need a
driving policy which can achieve desirable driving quality in long-term horizon
with guaranteed safety and feasibility. Optimization-based approaches, such as
Model Predictive Control (MPC), can provide such optimal policies, but their
computational complexity is generally unacceptable for real-time
implementation. To address this problem, we propose a fast integrated planning
and control framework that combines learning- and optimization-based approaches
in a two-layer hierarchical structure. The first layer, defined as the "policy
layer", is established by a neural network which learns the long-term optimal
driving policy generated by MPC. The second layer, called the "execution
layer", is a short-term optimization-based controller that tracks the reference
trajecotries given by the "policy layer" with guaranteed short-term safety and
feasibility. Moreover, with efficient and highly-representative features, a
small-size neural network is sufficient in the "policy layer" to handle many
complicated driving scenarios. This renders online imitation learning with
Dataset Aggregation (DAgger) so that the performance of the "policy layer" can
be improved rapidly and continuously online. Several exampled driving scenarios
are demonstrated to verify the effectiveness and efficiency of the proposed
framework
Shared control strategies for automated vehicles
188 p.Los vehículos automatizados (AVs) han surgido como una solución tecnológica para compensar las deficiencias de la conducción manual. Sin embargo, esta tecnología aún no está lo suficientemente madura para reemplazar completamente al conductor, ya que esto plantea problemas técnicos, sociales y legales. Sin embargo, los accidentes siguen ocurriendo y se necesitan nuevas soluciones tecnológicas para mejorar la seguridad vial. En este contexto, el enfoque de control compartido, en el que el conductor permanece en el bucle de control y, junto con la automatización, forma un equipo bien coordinado que colabora continuamente en los niveles táctico y de control de la tarea de conducción, es una solución prometedora para mejorar el rendimiento de la conducción manual aprovechando los últimos avances en tecnología de conducción automatizada. Esta estrategia tiene como objetivo promover el desarrollo de sistemas de asistencia al conductor más avanzados y con mayor grade de cooperatición en comparación con los disponibles en los vehículos comerciales. En este sentido, los vehículos automatizados serán los supervisores que necesitan los conductores, y no al revés. La presente tesis aborda en profundidad el tema del control compartido en vehículos automatizados, tanto desde una perspectiva teórica como práctica. En primer lugar, se proporciona una revisión exhaustiva del estado del arte para brindar una descripción general de los conceptos y aplicaciones en los que los investigadores han estado trabajando durante lasúltimas dos décadas. Luego, se adopta un enfoque práctico mediante el desarrollo de un controlador para ayudar al conductor en el control lateral del vehículo. Este controlador y su sistema de toma de decisiones asociado (Módulo de Arbitraje) se integrarán en el marco general de conducción automatizada y se validarán en una plataforma de simulación con conductores reales. Finalmente, el controlador desarrollado se aplica a dos sistemas. El primero para asistir a un conductor distraído y el otro en la implementación de una función de seguridad para realizar maniobras de adelantamiento en carreteras de doble sentido. Al finalizar, se presentan las conclusiones más relevantes y las perspectivas de investigación futuras para el control compartido en la conducción automatizada
Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios
Uncertainty in the behavior of other traffic participants is a crucial factor
in collision avoidance for automated driving; here, stochastic metrics should
often be considered to avoid overly conservative decisions. This paper
introduces a Stochastic Model Predictive Control (SMPC) planner for emergency
collision avoidance in highway scenarios to proactively minimize collision risk
while ensuring safety through chance constraints. To address the challenge of
guaranteeing the feasibility for the emergency trajectory, we incorporate
nonlinear tire dynamics in the prediction model of the ego vehicle. Further, we
exploit Max-Min-Plus-Scaling (MMPS) approximations of the nonlinearities to
avoid conservatism, enforce proactive collision avoidance, and improve
computational efficiency in terms of performance and speed. Consequently, our
contributions include integrating a dynamic ego vehicle model into the SMPC
planner, introducing the MMPS approximation for real-time implementation in
emergency scenarios, and integrating SMPC with hybridized chance constraints
and risk minimization. We evaluate our SMPC formulation in terms of proactivity
and efficiency in various hazardous scenarios. Moreover, we demonstrate the
effectiveness of our proposed approach by comparing it with a state-of-the-art
SMPC planner and validate the feasibility of generated trajectories using a
high-fidelity vehicle model in IPG CarMaker.Comment: 13 pages, 10 figures, submitted to IEEE Transactions on Control
Systems Technolog
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