scholarly journals Integrating the Intelligent Driver Model With the Action Point Paradigm to Enhance the Performance of Autonomous Driving

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 106284-106295
Author(s):  
Haifei Yang ◽  
Changjiang Zheng ◽  
Yi Zhao ◽  
Zhong Wu
Keyword(s):  
Autonomous Driving ◽  
Driver Model ◽  
Action Point

A motivational driver model for the design of a rear-end crash avoidance system

2019 ◽  
Vol 234 (1) ◽  
pp. 10-26
Author(s):  
Hamed Mozaffari ◽  
Ali Nahvi
Keyword(s):  
Driving Simulator ◽  
Human Subjects ◽  
Autonomous Driving ◽  
Driver Model ◽  
Driver Assistance Systems ◽  
Optimal Point ◽  
Time To Collision ◽  
Human Aspects ◽  
Crash Avoidance ◽  
Assistance Systems

A motivational driver model is developed to design a rear-end crash avoidance system. Current driver assistance systems use engineering methods without considering psychological human aspects, which leads to false activation of assistance systems and complicated control algorithms. The presented driver model estimates driver’s psychological motivations using the combined longitudinal and lateral time to collision, the vehicle kinematics, and the vehicle dynamics. These motivations simplify both autonomous driving algorithms and human-machine interactions. The optimal point of a motivational multi-objective cost function defines the decision for the autonomous driving. Moreover, the motivations are used as risk assessment factors for driver–machine interaction in dangerous situations. The system is evaluated on 10 human subjects in a driving simulator. The assistance system had no false activation during the tests. It avoided collisions in all the rear-end crash avoidance scenarios, while 90% of human subjects did not.

scholarly journals Modeling Car-Following Behaviors and Driving Styles with Generative Adversarial Imitation Learning

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5034
Author(s):  
Yang Zhou ◽  
Rui Fu ◽  
Chang Wang ◽  
Ruibin Zhang
Keyword(s):  
Autonomous Driving ◽  
Imitation Learning ◽  
Driver Model ◽  
Area Network ◽  
Car Following ◽  
Time Headway ◽  
Reward Function ◽  
Proposed Model ◽  
Wave Radar ◽  
Car Following Model

Building a human-like car-following model that can accurately simulate drivers’ car-following behaviors is helpful to the development of driving assistance systems and autonomous driving. Recent studies have shown the advantages of applying reinforcement learning methods in car-following modeling. However, a problem has remained where it is difficult to manually determine the reward function. This paper proposes a novel car-following model based on generative adversarial imitation learning. The proposed model can learn the strategy from drivers’ demonstrations without specifying the reward. Gated recurrent units was incorporated in the actor-critic network to enable the model to use historical information. Drivers’ car-following data collected by a test vehicle equipped with a millimeter-wave radar and controller area network acquisition card was used. The participants were divided into two driving styles by K-means with time-headway and time-headway when braking used as input features. Adopting five-fold cross-validation for model evaluation, the results show that the proposed model can reproduce drivers’ car-following trajectories and driving styles more accurately than the intelligent driver model and the recurrent neural network-based model, with the lowest average spacing error (19.40%) and speed validation error (5.57%), as well as the lowest Kullback-Leibler divergences of the two indicators used for driving style clustering.

Speed Trajectory Generation for Energy-Efficient Connected and Automated Vehicles

2020 ◽  
Author(s):  
Lung En Jan ◽  
Junfeng Zhao ◽  
Shunsuke Aoki ◽  
Anand Bhat ◽  
Chen-Fang Chang ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Network Models ◽  
Autonomous Driving ◽  
Urban Traffic ◽  
Sensor Data ◽  
Driver Model ◽  
Automated Vehicles ◽  
Vehicle Trajectories ◽  
Planning Framework ◽  
Vehicle To Infrastructure

Abstract Connected and automated vehicles (CAVs) have real-time knowledge of the immediate driving environment, actions to be taken in the near future and information from the cloud. This knowledge, referred to as preview information, enables CAVs to drive safely, but can also be used to minimize fuel consumption. Such fuel-efficient transportation has the potential to reduce aggregate fuel consumption by billions of gallons of gas every year in the U.S. alone. In this paper, we propose a planning framework for use in CAVs with the goal of generating fuel-efficient vehicle trajectories. By utilizing on-board sensor data and vehicle-to-infrastructure (V2I) communications, we leverage the computational power of CAVs to generate eco-friendly vehicle trajectories. The planner uses an eco-driver model and a predictive cost-based search to determine the optimal speed profile for use by a CAV. To evaluate the performance of the planner, we introduce a co-simulation environment consisting of a CAV simulator, Matlab/Simulink and a CAV software platform called the InfoRich Eco-Autonomous Driving (iREAD) system. The planner is evaluated in various urban traffic scenarios based on real-world road network models provided by the National Renewable Energy Laboratory (NREL). Simulations show an average savings of 14.5% in fuel consumption with a corresponding increase of 2% in travel time using our method.

Driver intention prediction using model-added Bayesian network

2020 ◽  
pp. 095440702096896
Author(s):  
Ruitao Song
Keyword(s):  
Bayesian Network ◽  
Autonomous Driving ◽  
Driver Model ◽  
Prediction Algorithm ◽  
Data Sets ◽  
Lane Change ◽  
Potential Threat ◽  
Surrounding Environment ◽  
Reliable Detection ◽  
Driver’S Intention

The autonomous driving technology requires reliable detection and prediction of the surrounding environment. Predicting the lane change intention of the surrounding traffic is critical to evaluate the potential threat around the host vehicle. This paper develops a lane change maneuver prediction algorithm based on a newly proposed driver model combined with a Bayesian network. The innovation of the proposed algorithm is the utilization of the driver model while calibrating and executing the Bayesian network. The prediction algorithm can provide not only the driver’s intention but also the probability associated with the intention. The Next-Generation Simulation data sets are used to develop and validate the prediction model. In total, there are more than 2000 lane change events used in this paper. The result shows that the proposed prediction algorithm can provide an accurate prediction of the surrounding vehicle’s lane change maneuver.

scholarly journals Prototyping Smart Assistance with Bayesian Autonomous Driver Models

2011 ◽  
pp. 460-512 ◽  
Author(s):  
Claus Moebus ◽  
Mark Eilers
Keyword(s):  
Human Behavior ◽  
Autonomous Driving ◽  
Training Data ◽  
Driver Model ◽  
Critical Question ◽  
Human Driver ◽  
Markov Decision ◽  
Traffic Scenario ◽  
Assistance Systems ◽  
Game Simulation

The Human or Cognitive Centered Design (HCD) of intelligent transport systems requires digital Models of Human Behavior and Cognition (MHBC) enabling Ambient Intelligence e.g. in a smart car. Currently MBHC are developed and used as driver models in traffic scenario simulations, in proving safety assertions and in supporting risk-based design. Furthermore, it is tempting to prototype assistance systems (AS) on the basis of a human driver model cloning an expert driver. To that end we propose the Bayesian estimation of MHBCs from human behavior traces generated in new kind of learning experiments: Bayesian model learning under driver control. The models learnt are called Bayesian Autonomous Driver (BAD) models. For the purpose of smart assistance in simulated or real world scenarios the obtained BAD models can be used as Bayesian Assistance Systems (BAS). The critical question is, whether the driving competence of the BAD model is the same as the driving competence of the human driver when generating the training data for the BAD model. We believe that our approach is superior to the proposal to model the strategic and tactical skills of an AS with a Markov Decision Process (MDP). The usage of the BAD model or BAS as a prototype for a smart Partial Autonomous Driving Assistant System (PADAS) is demonstrated within a racing game simulation.

scholarly journals Design and Numerical Implementation of V2X Control Architecture for Autonomous Driving Vehicles

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1696
Author(s):  
Piyush Dhawankar ◽  
Prashant Agrawal ◽  
Bilal Abderezzak ◽  
Omprakash Kaiwartya ◽  
Krishna Busawon ◽  
...  
Keyword(s):  
Control System ◽  
Autonomous Driving ◽  
Driver Model ◽  
Ieee 802.11P ◽  
Packet Error Rate ◽  
Control Architecture ◽  
Practical Implementation ◽  
Infinite Length ◽  
Estimation Model ◽  
Vehicle To Infrastructure

This paper is concerned with designing and numerically implementing a V2X (Vehicle-to-Vehicle and Vehicle-to-Infrastructure) control system architecture for a platoon of autonomous vehicles. The V2X control architecture integrates the well-known Intelligent Driver Model (IDM) for a platoon of Autonomous Driving Vehicles (ADVs) with Vehicle-to-Infrastructure (V2I) Communication. The main aim is to address practical implementation issues of such a system as well as the safety and security concerns for traffic environments. To this end, we first investigated a channel estimation model for V2I communication. We employed the IEEE 802.11p vehicular standard and calculated path loss, Packet Error Rate (PER), Signal-to-Noise Ratio (SNR), and throughput between transmitter and receiver end. Next, we carried out several case studies to evaluate the performance of the proposed control system with respect to its response to: (i) the communication infrastructure; (ii) its sensitivity to an emergency, inter-vehicular gap, and significant perturbation; and (iii) its performance under the loss of communication and changing driving environment. Simulation results show the effectiveness of the proposed control model. The model is collision-free for an infinite length of platoon string on a single lane road-driving environment. It also shows that it can work during a lack of communication, where the platoon vehicles can make their decision with the help of their own sensors. V2X Enabled Intelligent Driver Model (VX-IDM) performance is assessed and compared with the state-of-the-art models considering standard parameter settings and metrics.

The study of a unified driver model controller based on fractional-order PIλDμ and internal model control

2020 ◽  
pp. 095440702096464
Author(s):  
Yan Ti ◽  
Rong Wang ◽  
Tinglun Song ◽  
Wanzhong Zhao
Keyword(s):  
Fractional Order ◽  
Internal Model ◽  
Autonomous Driving ◽  
Path Tracking ◽  
Internal Model Control ◽  
Driver Model ◽  
Key Factors ◽  
Simulink Simulation ◽  
Model Control ◽  
Driving Stability

Autonomous driving has been one of the key factors behind the various technology innovation initiatives in the automotive industry in recent years. A unified driver model controller, which is essential to the design and development of autonomous driving technology, based on fractional-order PIλDμ and internal model control is proposed and studied in this paper. The unified driver model control algorithm utilizes and integrates fractional-order PIλDμ control and internal model control to ensure path tracking capability and driving stability of the vehicle. Matlab/Simulink simulation of the proposed controller indicates that it can effectively improve path tracking capabilities and driving stability.

The study of a unified driver model controller based on fractional-order PIλDμ and internal model control

2020 ◽  
pp. 095440702096843
Author(s):  
Yan Ti ◽  
Rong Wang ◽  
Tinglun Song ◽  
Wanzhong Zhao
Keyword(s):  
Fractional Order ◽  
Internal Model ◽  
Autonomous Driving ◽  
Path Tracking ◽  
Internal Model Control ◽  
Driver Model ◽  
Key Factors ◽  
Simulink Simulation ◽  
Model Control ◽  
Driving Stability

Autonomous driving has been one of the key factors behind the various technology innovation initiatives in the automotive industry in recent years. A unified driver model controller, which is essential to the design and development of autonomous driving technology, based on fractional-order PIλDμ and internal model control is proposed and studied in this paper. The unified driver model control algorithm utilizes and integrates fractional-order PIλDμ control and internal model control to ensure path tracking capability and driving stability of the vehicle. Matlab/Simulink simulation of the proposed controller indicates that it can effectively improve path tracking capabilities and driving stability.

scholarly journals A Fuzzy Logic-Based Approach for Humanized Driver Modelling

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yuxiang Feng ◽  
Pejman Iravani ◽  
Chris Brace
Keyword(s):  
Fuzzy Logic ◽  
Autonomous Vehicles ◽  
Control Strategies ◽  
Cost Effective ◽  
Autonomous Driving ◽  
Driver Model ◽  
Vehicle Model ◽  
Car Following ◽  
Wide Range ◽  
Instrumented Vehicle

All human drivers can be characterised by their habitual choice of driving behaviours, which results in a wide range of observed driving patterns and manoeuvres. Developing control strategies for autonomous vehicles that address this feature would increase the public acceptance of such vehicles. Therefore, this paper proposes a novel approach to developing rule-based fuzzy logic driver models that simulate different driving styles in the car-following regimes. These driver models were trained with the collected on-road driving data to capture corresponding human drivers’ characteristics. The proposed approach consists of three main components: collecting on-road driving data, developing a vehicle model, and establishing the car-following driver models. Firstly, an instrumented vehicle was used to collect driving data over the same route for three consecutive months. Car-following scenarios during these journeys were extracted, and related data were processed accordingly. Afterwards, a representative model of the instrumented vehicle was created and evaluated. Finally, a fuzzy logic driver model that uses humanized inputs was developed and calibrated with the recorded data. The developed driver model’s performance was assessed using the collected driving data and a baseline PID driver model. With the performance validated, models representing more aggressive and more defensive driving styles were derived following the same procedure. A cross-driver analysis was then implemented in a normalized car-following scenario with the established vehicle model to investigate the impacts of different driving styles further. The developed driver model can introduce driving styles into drive cycle experiments and replicate on-road real driving emission tests in the laboratory. Moreover, as the proposed method has high robustness to incomplete datasets, it can be a more cost-effective option to facilitate the development of humanized and customized vehicle control strategies for autonomous driving.

The study of a unified driver model controller based on fractional-order PIλDμ and internal model control

2020 ◽  
pp. 095440702097124
Author(s):  
Yan Ti ◽  
Rong Wang ◽  
Tinglun Song ◽  
Wanzhong Zhao
Keyword(s):  
Fractional Order ◽  
Internal Model ◽  
Autonomous Driving ◽  
Path Tracking ◽  
Internal Model Control ◽  
Driver Model ◽  
Key Factors ◽  
Simulink Simulation ◽  
Model Control ◽  
Driving Stability

Autonomous driving has been one of the key factors behind the various technology innovation initiatives in the automotive industry in recent years. A unified driver model controller, which is essential to the design and development of autonomous driving technology, based on fractional-order PIλDμ and internal model control is proposed and studied in this paper. The unified driver model control algorithm utilizes and integrates fractional-order PIλDμ control and internal model control to ensure path tracking capability and driving stability of the vehicle. Matlab/Simulink simulation of the proposed controller indicates that it can effectively improve path tracking capabilities and driving stability.

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