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 Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1372-1376
Author(s):  
Gaojian Huang ◽  
Christine Petersen ◽  
Brandon J. Pitts
Keyword(s):  
Systematic Review ◽  
Autonomous Vehicles ◽  
Response Times ◽  
Autonomous Driving ◽  
Mental States ◽  
Manual Control ◽  
Monitoring Systems ◽  
Wide Range ◽  
Different Types ◽  
The Impact

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

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 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.

PERTURBATION AND STABILITY ANALYSIS OF THE MULTI-ANTICIPATIVE INTELLIGENT DRIVER MODEL

2010 ◽  
Vol 21 (05) ◽  
pp. 647-668 ◽  
Author(s):  
XI-QUN CHEN ◽  
WEI-JUN XIE ◽  
JING SHI ◽  
QI-XIN SHI
Keyword(s):  
Stability Analysis ◽  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Driver Model ◽  
Stable Region ◽  
Local Perturbation ◽  
Distance Information ◽  
Car Following ◽  
Simulation Results ◽  
The Stability

This paper discusses three kinds of IDM car-following models that consider both the multi-anticipative behaviors and the reaction delays of drivers. Here, the multi-anticipation comes from two ways: (1) the driver is capable of evaluating the dynamics of several preceding vehicles, and (2) the autonomous vehicles can obtain the velocity and distance information of several preceding vehicles via inter-vehicle communications. In this paper, we study the stability of homogeneous traffic flow. The linear stability analysis indicates that the stable region will generally be enlarged by the multi-anticipative behaviors and reduced by the reaction delays. The temporal amplification and the spatial divergence of velocities for local perturbation are also studied, where the results further prove this conclusion. Simulation results also show that the multi-anticipative behaviors near the bottleneck will lead to a quicker backwards propagation of oscillations.

scholarly journals MEMS Mirrors for LiDAR: A Review

Micromachines ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 456 ◽  
Author(s):  
Dingkang Wang ◽  
Connor Watkins ◽  
Huikai Xie
Keyword(s):  
Autonomous Vehicles ◽  
Figure Of Merit ◽  
Three Dimensional ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Field Of View ◽  
Preliminary Assessment ◽  
Wide Range ◽  
Laser Scanners ◽  
Increasing Demand

In recent years, Light Detection and Ranging (LiDAR) has been drawing extensive attention both in academia and industry because of the increasing demand for autonomous vehicles. LiDAR is believed to be the crucial sensor for autonomous driving and flying, as it can provide high-density point clouds with accurate three-dimensional information. This review presents an extensive overview of Microelectronechanical Systems (MEMS) scanning mirrors specifically for applications in LiDAR systems. MEMS mirror-based laser scanners have unrivalled advantages in terms of size, speed and cost over other types of laser scanners, making them ideal for LiDAR in a wide range of applications. A figure of merit (FoM) is defined for MEMS mirrors in LiDAR scanners in terms of aperture size, field of view (FoV) and resonant frequency. Various MEMS mirrors based on different actuation mechanisms are compared using the FoM. Finally, a preliminary assessment of off-the-shelf MEMS scanned LiDAR systems is given.

scholarly journals On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

2021 ◽  
Vol 11 (5) ◽  
pp. 2197
Author(s):  
Stefania Santini ◽  
Nicola Albarella ◽  
Vincenzo Maria Arricale ◽  
Renato Brancati ◽  
Aleksandr Sakhnevych
Keyword(s):  
Environmental Conditions ◽  
Autonomous Vehicles ◽  
Heavy Rain ◽  
Autonomous Driving ◽  
The State ◽  
Driver Assistance Systems ◽  
Model Based ◽  
Wide Range ◽  
Road Friction ◽  
Friction Estimation

In recent years, autonomous vehicles and advanced driver assistance systems have drawn a great deal of attention from both research and industry, because of their demonstrated benefit in reducing the rate of accidents or, at least, their severity. The main flaw of this system is related to the poor performances in adverse environmental conditions, due to the reduction of friction, which is mainly related to the state of the road. In this paper, a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The proposed technique is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach to evaluate the potential friction. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. The effectiveness of the proposed approach is disclosed via an extensive numerical analysis covering a wide range of environmental, traffic, and vehicle kinematic conditions. Results confirm the ability of the approach to properly automatically adapting the inter-vehicle space gap and to avoiding collisions also in adverse road conditions (e.g., ice, heavy rain).

scholarly journals Simulating the Impact of Shared, Autonomous Vehicles on Urban Mobility – a Case Study of Milan

10.29007/2n4h ◽  
2018 ◽  
Author(s):  
Sabina Alazzawi ◽  
Mathias Hummel ◽  
Pascal Kordt ◽  
Thorsten Sickenberger ◽  
Christian Wieseotte ◽  
...  
Keyword(s):  
Dynamic Pricing ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Transition Rate ◽  
Waiting Times ◽  
Region Of Interest ◽  
Autonomous Driving ◽  
Flow State ◽  
Wide Range ◽  
The Impact

Recent technological advances in vehicle automation and connectivity have furthered the development of a wide range of innovative mobility concepts such as autonomous driving, on-demand services and electric mobility. Our study aimed at investigating the interplay of these concepts to efficiently reduce vehicle counts in urban environments, thereby reducing congestion levels and creating new public spaces to promote the quality of live in urban cities. For analysis, we implemented the aforementioned factors by introducing the concept of robo-taxis as an autonomous and shared mobility service. Using SUMO as the simulation framework, custom functionalities such as ride sharing, autonomous driving and advanced data processing were implemented as python methods via, and around, the TraCI interface. A passenger origin-destination matrix for our region of interest in Milan was derived from publically available mobile phone usage data and used for route input. Key evaluation parameters were the density-flow relationship, particulate-matter emissions, and person waiting- times. Based on these parameters, the critical transition rate from private cars to robo- taxis to reach a free-flow state was calculated. Our simulations show, that a transition rate of about 50% is required to achieve a significant reduction of traffic congestion levels in peak hours as indicated by mean travel times and vehicle flux. Assuming peak- shaving, e.g. through dynamic pricing promised by digitalization, of about 10%, the threshold transition rate drops to 30%. Based on these findings, we propose that introducing a robo-taxi fleet of 9500 vehicles, centered around mid-size 6 seaters, can solve traffic congestion and emission problems in Milan.

scholarly journals Redundant Multi-Object Detection for Autonomous Vehicles in Structured Environments

2022 ◽  
Vol 24 (1) ◽  
pp. C1-C17
Author(s):  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Nicola Amati ◽  
Andrea Tonoli
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Single Shot ◽  
Custom Hardware ◽  
Instrumented Vehicle ◽  
Deep Learning Neural Network ◽  
Local Map

This paper presents a redundant multi-object detection method for autonomous driving, exploiting a combination of Light Detection and Ranging (LiDAR) and stereocamera sensors to detect different obstacles. These sensors are used for distinct perception pipelines considering a custom hardware/software architecture deployed on a self-driving electric racing vehicle. Consequently, the creation of a local map with respect to the vehicle position enables development of further local trajectory planning algorithms. The LiDAR-based algorithm exploits segmentation of point clouds for the ground filtering and obstacle detection. The stereocamerabased perception pipeline is based on a Single Shot Detector using a deep learning neural network. The presented algorithm is experimentally validated on the instrumented vehicle during different driving maneuvers.

scholarly journals Visibility-Based Technologies and Methodologies for Autonomous Driving

2020 ◽  
Author(s):  
Said Easa ◽  
Yang Ma ◽  
Ashraf Elshorbagy ◽  
Ahmed Shaker ◽  
Songnian Li ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Visual Space ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Car Following ◽  
Sight Distance ◽  
Mapping System ◽  
3D Mapping System ◽  
Highway Alignment ◽  
Mapping Scheme

The three main elements of autonomous vehicles (AV) are orientation, visibility, and decision. This chapter presents an overview of the implementation of visibility-based technologies and methodologies. The chapter first presents two fundamental aspects that are necessary for understanding the main contents. The first aspect is highway geometric design as it relates to sight distance and highway alignment. The second aspect is mathematical basics, including coordinate transformation and visual space segmentation. Details on the Light Detection and Ranging (Lidar) system, which represents the ‘eye’ of the AV are presented. In particular, a new Lidar 3D mapping system, that can be operated on different platforms and modes for a new mapping scheme is described. The visibility methodologies include two types. Infrastructure visibility mainly addresses high-precision maps and sight obstacle detection. Traffic visibility (vehicles, pedestrians, and cyclists) addresses identification of critical positions and visibility estimation. Then, an overview of the decision element (path planning and intelligent car-following) for the movement of AV is presented. The chapter provides important information for researchers and therefore should help to advance road safety for autonomous vehicles.

scholarly journals Enery management in Mobile Hydraulics

2013 ◽  
Vol 135 (06) ◽  
pp. S4-S6
Author(s):  
Andrew Alleyne ◽  
Timothy Deppen ◽  
Jonathan Meyer ◽  
Kim Stelson
Keyword(s):  
Energy Management ◽  
Control Strategies ◽  
Management Strategies ◽  
Electrical Power ◽  
Cost Effective ◽  
Transportation Systems ◽  
Fluid Power ◽  
Energy Management Strategy ◽  
Hybrid Powertrain ◽  
Wide Range

This paper explores research into hydraulic hybrids that span a wide range of applications from heavy-duty vehicles, such as city buses, to small passenger vehicles. This case study also highlights the importance of having a well-designed energy management strategy if one is to maximize benefit of the hybrid powertrain. There is potential for hydraulic hybrid vehicles to offer a cost-effective solution to the need for increased efficiency in transportation systems. The high-power density of fluid power makes it a natural choice for energy storage in urban driving environments where there are frequent starts/stops and large acceleration/braking power demands. Because the opportunities and challenges of fluid power are different than those of electrical power, unique control strategies are needed and a summary of common energy management strategies (EMS) design methods for hydraulic hybrids has been presented.

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