Modeling of Individualized Human Driver Model for Automated Personalized Supervision

2010 ◽  
Author(s):  
Xingguang Fu ◽  
Dirk Soeffker
Keyword(s):  
Driver Model ◽  
Human Driver

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.

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.

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.

2P2-E33 Identification of Human Driver Model for a Universal ITS Simulator

2006 ◽  
Vol 2006 (0) ◽  
pp. _2P2-E33_1-_2P2-E33_4
Author(s):  
Keisuke KONDO ◽  
Hideki MIYAMOTO ◽  
Nathan A. Webster ◽  
Masaaki ONUKI ◽  
Takahiro SUZUKI ◽  
...  
Keyword(s):  
Driver Model ◽  
Human Driver

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.

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