Development of an Emergency Lane Change System in Highway Driving

2016 ◽  
Author(s):  
Serdar Coskun ◽  
Reza Langari
Keyword(s):  
Decision Process ◽  
Transition Probabilities ◽  
Driver Model ◽  
Effective Control ◽  
Control Input ◽  
Continuous Control ◽  
Lane Change ◽  
Decision Strategy ◽  
Human Driver ◽  
The Subject

This paper presents an approach to the lane change safety system for collision avoidance. The solution is presented in two distinct steps. We first propose a decision strategy based on a discrete time Markov process to determine the safe lane utilizing a set of transition probabilities. These probabilities are calculated according to the distance of the subject vehicle from the surrounding vehicles. The output of decision process is fed to a controller formulated using an ℋ∞ scheme to move the vehicle to the desired lane. The overall strategy can be viewed as a combination of continuous control with a discrete decision process. The performance of the proposed scheme is compared with the so-called human-driver model (HDM) based control, which has been broadly discussed in the literature. The simulation study shows the superiority of the proposed controller in terms of trajectory tracking of the reference path, disturbance rejection of the wind load, and effective control input.

Brain Limbic System Based Robust Control for Lane Change Maneuver

2010 ◽  
Author(s):  
Changwon Kim ◽  
Reza Langari
Keyword(s):  
Intelligent Control ◽  
Limbic System ◽  
Driver Model ◽  
Lane Change ◽  
Lateral Dynamics ◽  
Human Driver ◽  
Control Scheme ◽  
Suggested Strategy ◽  
Simulation Results ◽  
The Brain

This paper presents the application of a novel intelligent control strategy for lane change maneuvers in highway environment. The lateral dynamics of a vehicle with and without wind disturbance are derived and utilized to implement a neuromophic controller based on the brain limbic system. To show the robustness of the proposed controller, several disturbance conditions including wind, uncertainty in the cornering stiffness, and changes in the vehicle mass, are investigated. To demonstrate the performance of the suggested strategy, the simulation results of the proposed method were compared with the human driver model based control scheme, which has been discussed in the literature. The simulation results demonstrate the superiority of the proposed controller in energy efficiency, driving comfort, and robustness.

Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields

Robotica ◽  
2017 ◽  
Vol 36 (4) ◽  
pp. 463-483 ◽  
Author(s):  
C. Ton ◽  
Z. Kan ◽  
S. S. Mehta
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Sliding Mode ◽  
Harmonic Potential ◽  
Control Allocation ◽  
Control Input ◽  
Continuous Control ◽  
Control Effort ◽  
Human In The Loop ◽  
Allocation Function

SUMMARYThis paper considers applications where a human agent is navigating a semi-autonomous mobile robot in an environment with obstacles. The human input to the robot can be based on a desired navigation objective, which may not be known to the robot. Additionally, the semi-autonomous robot can be programmed to ensure obstacle avoidance as it navigates the environment. A shared control architecture can be used to appropriately fuse the human and the autonomy inputs to obtain a net control input that drives the robot. In this paper, an adaptive, near-continuous control allocation function is included in the shared controller, which continuously varies the control effort exerted by the human and the autonomy based on the position of the robot relative to obstacles. The developed control allocation function facilitates the human to freely navigate the robot when away from obstacles, and it causes the autonomy control input to progressively dominate as the robot approaches obstacles. A harmonic potential field-based non-linear sliding mode controller is developed to obtain the autonomy control input for obstacle avoidance. In addition, a robust feed-forward term is included in the autonomy control input to maintain stability in the presence of adverse human inputs, which can be critical in applications such as to prevent collision or roll-over of smart wheelchairs due to erroneous human inputs. Lyapunov-based stability analysis is presented to guarantee finite-time stability of the developed shared controller, i.e., the autonomy guarantees obstacle avoidance as the human navigates the robot. Experimental results are provided to validate the performance of the developed shared controller.

scholarly journals Nelder-Mead Optimized MADM Decision Support for Vertical Handover

2019 ◽  
Vol 8 (2S3) ◽  
pp. 601-604
Keyword(s):  
Decision Process ◽  
Vertical Handover ◽  
Network Selection ◽  
Heterogeneous Environment ◽  
Challenging Problem ◽  
Wireless Technology ◽  
Decision Strategy ◽  
Handover Decision ◽  
Required Quality

The vertical handover decision strategy is a challenging problem to support seamless mobility in different wireless technology. The handover decision process is reported to facilitate the required Quality of Service, but bad network selection and overload condition of the chosen network fallout in handover failures. Traditional handoff techniques typically are based on single parameter and are inefficient due to superfluous handovers. In addition, existing fuzzy based handoff method reduces the handover failures but increase the computational complexity. Furthermore, the existing mobile controlled handover techniques are not suitable for heterogeneous environment. Therefore this paper focus the need of efficient, robust and flexible vertical handover technique to reduce the handover failures and design an MADM based network controlled handover that maximize QoS in heterogeneous environment

scholarly journals Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes

2019 ◽  
Vol 11 (6) ◽  
pp. 168781401985978
Author(s):  
Ja-Ho Seo ◽  
Kwang-Seok Oh ◽  
Hong-Jun Noh
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Steering System ◽  
Driver Model ◽  
Control Technique ◽  
Change Scenario ◽  
Steering Control ◽  
Human Driver ◽  
Steering Effort

All-terrain cranes with multi-axles have large inertia and long distances between the axles that lead to a slower dynamic response than normal vehicles. This has a significant effect on the dynamic behavior and steering performance of the crane. Therefore, the purpose of this study is to develop an optimal steering control algorithm with a reduced driver steering effort for an all-terrain crane and to evaluate the performance of the algorithm. For this, a model predictive control technique was applied to an all-terrain crane, and a steering control algorithm for the crane was proposed that could reduce the driver’s steering effort. The steering performances of the existing steering system and the steering system applied with the newly developed algorithm were compared using MATLAB/Simulink and ADAMS with a human driver model for reasonable performance evaluation. The simulation was performed with both a double lane change scenario and a curved-path scenario that are expected to happen in road-steering mode.

scholarly journals Development of Deep Learning Based Human-Centered Threat Assessment for Application to Automated Driving Vehicle

2019 ◽  
Vol 10 (1) ◽  
pp. 253 ◽  
Author(s):  
Donghoon Shin ◽  
Hyun-geun Kim ◽  
Kang-moon Park ◽  
Kyongsu Yi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Threat Assessment ◽  
Collision Probability ◽  
Driver Model ◽  
Sequential Data ◽  
Automated Driving ◽  
Human Driver ◽  
Acceleration And Deceleration ◽  
The Individual

This paper describes the development of deep learning based human-centered threat assessment for application to automated driving vehicle. To achieve naturalistic driver model that would feel natural while safe to a human driver, manual driving characteristics are investigated through real-world driving test data. A probabilistic threat assessment with predicted collision time and collision probability is conducted to evaluate driving situations. On the basis of collision risk analysis, two kinds of deep learning have been implemented to reflect human driving characteristics for automated driving. A deep neural network (DNN) and recurrent neural network (RNN) are designed by neural architecture search (NAS), and by learning from the sequential data, respectively. The NAS is used to automatically design the individual driver’s neural network for efficient and effortless design process while ensuring training performance. Sequential trends in the host vehicle’s state can be incorporated through hand-made RNN. It has been shown from human-centered risk assessment simulations that two successfully designed deep learning driver models can provide conservative and progressive driving behavior similar to a manual human driver in both acceleration and deceleration situations by preventing collision.

scholarly journals Automatic Parking Controller with a Twin Artificial Neural Network Architecture

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Jaeyoung Moon ◽  
Il Bae ◽  
Shiho Kim
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Control Laws ◽  
Confined Spaces ◽  
Human Driver ◽  
Kinematic Models ◽  
Automatic Parking ◽  
The Subject ◽  
Traditional Approaches

We propose an artificial deep neural network- (ANN-) based automatic parking controller that overcomes a stubborn restriction prevalent in traditional approaches. The proposed ANN learns human-like control laws for automatic parking through supervised learning from a training database generated by computer-aided optimizations or real experiments. By learning the relationships between the instantaneous vehicle states and the corresponding maneuver parameters, the proposed twin controller yields lateral and longitudinal maneuvering parameters for executing automatic parking tasks in confined spaces. The proposed automatic parking controller exhibits a twin architecture comprising a main agent and its cloned agent. Before the main agent assumes a maneuvering action, the cloned agent predicts the consequences of the maneuvering action through a Collision Checking and Adjustment (CCA) system. The proposed parking agent operates like a human driver in a manner that is characterized by an unplanned trajectory. In addition, the kinematics of the subject vehicle is not exactly modelled for parking control. The simulation results demonstrate that the proposed twin agent emulates the attributes of a human driver such as adaptive control and determines the consequences of the tentative maneuvering action under varying kinematic models of the subject vehicle. We validate the proposed parking controller by simulating the software-in-the-loop architecture using a PreScan simulator in which the dynamics of the virtual vehicle’s behavior resemble a real vehicle.

Human Driver Modeling Based on Analytical Optimal Solutions: Stopping Behaviors at the Intersections

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Jihun Han ◽  
Dominik Karbowski ◽  
Namdoo Kim ◽  
Aymeric Rousseau
Keyword(s):  
Optimal Control ◽  
State Constraint ◽  
Driver Model ◽  
Optimal Solutions ◽  
Driving Behaviors ◽  
Human Driver ◽  
Proposed Model ◽  
Wide Range ◽  
Instrumented Vehicle ◽  
Road Testing

Abstract Safe and energy-efficient driving of connected and automated vehicles (CAVs) must be influenced by human-driven vehicles. Thus, to properly evaluate the energy impacts of CAVs in a simulation framework, a human driver model must capture a wide range of real-world driving behaviors corresponding to the surrounding environment. This paper formulates longitudinal human driving as an optimal control problem with a state constraint imposed by the vehicle in front. Deriving analytically optimal solutions by employing optimal control theory can capture longitudinal human driving behaviors with low computational burden, and adding the state constraint can assist with describing car-following features while anticipating behaviors of the vehicle in front. We also use on-road testing data collected by an instrumented vehicle to validate the proposed human driver model for stop scenarios at intersections. Results show that vehicle stopping trajectories of the proposed model are well matched with those of experimental data.

scholarly journals Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2259 ◽  
Author(s):  
Chang Wang ◽  
Qinyu Sun ◽  
Zhen Li ◽  
Hongjia Zhang
Keyword(s):  
Risk Perception ◽  
Autonomous Vehicles ◽  
Decision Model ◽  
Signal Detection Theory ◽  
Critical Issue ◽  
Lane Change ◽  
Mixed Traffic ◽  
Driving System ◽  
Lane Changes ◽  
The Subject

Determining an appropriate time to execute a lane change is a critical issue for the development of Autonomous Vehicles (AVs).However, few studies have considered the rear and the front vehicle-driver’s risk perception while developing a human-like lane-change decision model. This paper aims to develop a lane-change decision model for AVs and to identify a two level threshold that conforms to a driver’s perception of the ability to safely change lanes with a rear vehicle approaching fast. Based on the signal detection theory and extreme moment trials on a real highway, two thresholds of safe lane change were determined with consideration of risk perception of the rear and the subject vehicle drivers, respectively. The rear vehicle’s Minimum Safe Deceleration (MSD) during the lane change maneuver of the subject vehicle was selected as the lane change safety indicator, and was calculated using the proposed human-like lane-change decision model. The results showed that, compared with the driver in the front extreme moment trial, the driver in the rear extreme moment trial is more conservative during the lane change process. To meet the safety expectations of the subject and rear vehicle drivers, the primary and secondary safe thresholds were determined to be 0.85 m/s2 and 1.76 m/s2, respectively. The decision model can help make AVs safer and more polite during lane changes, as it not only improves acceptance of the intelligent driving system, but also further ensures the rear vehicle’s driver’s safety.

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