Probabilistic Modeling of Driver Behaviors at Urban Crossroad Interactions

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Yuan-Cheng Liu ◽  
Kuei-Yuan Chan
Keyword(s):  
Virtual Environments ◽  
Autonomous Vehicles ◽  
State Of The Art ◽  
Autonomous Vehicle ◽  
Vehicle Speed ◽  
Crash Analysis ◽  
Characteristic Parameters ◽  
Human Behaviors ◽  
Simulated Environments ◽  
Traffic Characteristic

Abstract The interactions with human drivers is one of the major challenges for autonomous vehicles. In this study, we consider urban crossroads without signals where driver interactions are indispensable. Crossroads are parameterized to be used in studying how drivers pass the crossroad while maintaining a desired speed without collision. We define a probability of yielding for each car as a function of vehicle speed and the distance-to-intersection for both vehicles, while the interactions between vehicles are characterized by a point of action for incoming vehicles from different directions. Driver behaviors in terms of acceleration/deceleration given current circumstances are also modeled probabilistically. The method is then analyzed and validated by data collected from human drivers in the simulated environments. The result shows comparable prediction accuracy to the state-of-the-art method, where characteristic parameters of drivers are also shown to be critical for the behavior predictions. We also extend our model to two real-world urban crossroads applications : crash analysis and traffic characteristic parameters identification. In both cases, our prediction results are analogous to those acquired in virtual environments. For autonomous vehicle, our method can help building a computer-driving logic that matches human behaviors, such that interactions between different drivers will be more intuitive.

scholarly journals An Overview of Lidar Imaging Systems for Autonomous Vehicles

2019 ◽  
Vol 9 (19) ◽  
pp. 4093 ◽  
Author(s):  
Santiago Royo ◽  
Maria Ballesta-Garcia
Keyword(s):  
Autonomous Vehicles ◽  
State Of The Art ◽  
Autonomous Vehicle ◽  
Single Point ◽  
Imaging Systems ◽  
Light Sources ◽  
Final Solution ◽  
Large Uncertainty ◽  
Current State ◽  
The Subject

Lidar imaging systems are one of the hottest topics in the optronics industry. The need to sense the surroundings of every autonomous vehicle has pushed forward a race dedicated to deciding the final solution to be implemented. However, the diversity of state-of-the-art approaches to the solution brings a large uncertainty on the decision of the dominant final solution. Furthermore, the performance data of each approach often arise from different manufacturers and developers, which usually have some interest in the dispute. Within this paper, we intend to overcome the situation by providing an introductory, neutral overview of the technology linked to lidar imaging systems for autonomous vehicles, and its current state of development. We start with the main single-point measurement principles utilized, which then are combined with different imaging strategies, also described in the paper. An overview of the features of the light sources and photodetectors specific to lidar imaging systems most frequently used in practice is also presented. Finally, a brief section on pending issues for lidar development in autonomous vehicles has been included, in order to present some of the problems which still need to be solved before implementation may be considered as final. The reader is provided with a detailed bibliography containing both relevant books and state-of-the-art papers for further progress in the subject.

scholarly journals Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Robotics ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 67 ◽  
Author(s):  
Aakash Soni ◽  
Huosheng Hu
Keyword(s):  
Autonomous Vehicles ◽  
Formation Control ◽  
State Of The Art ◽  
Autonomous Vehicle ◽  
Ground Vehicles ◽  
Implementation Challenges ◽  
Autonomous Ground Vehicles ◽  
Vehicle Platooning ◽  
And Control

Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized.

Stability of Controlled Road Vehicles: A Preliminary Fundamental Study

2015 ◽  
Author(s):  
Fabio della Rossa ◽  
Massimiliano Gobbi ◽  
Giampiero Mastinu ◽  
Carlo Piccardi ◽  
Giorgio Previati
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Preliminary Investigation ◽  
Basin Of Attraction ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Vehicle Model ◽  
The Stability ◽  
Positive Effect

A comparison of the lateral stability behaviour between an autonomous vehicle, a vehicle with driver and a vehicle without driver (fixed steering wheel) is made by introducing a simple mathematical model of a vehicle running on even road. The mechanical model of the vehicle has two degrees of freedom and the related equations of motion contain the nonlinear tyre characteristics. The driver is described by a well-known model proposed in the literature. The autonomous vehicle has a virtual driver (robot) that behaves substantially like a human, but with its proper reaction time and gain. The road vehicle model has been validated. The study of vehicle stability has to be based on bifurcation analysis and a preliminary investigation is proposed here. The accurate computation of steady-state equilibria is crucial to study the stability of the three kinds of vehicles here compared. The stability of the bare vehicle without driver (fixed steering wheel) is studied in a rather complete way referring to a number of combinations of tyre characteristics. The (known) conclusion is that the understeering vehicle is stable at each lateral acceleration level and at each vehicle speed. The additional (partially unknown) conclusion is that the vehicle (model) with degradated tyres may exhibit a huge number of different bifurcations. The driver has many effects on the stability of the vehicle. One positive effect is to eliminate the many possible different equilibria of the bare vehicle and keep active one single equilibrium only. Another positive effect is to broaden the basin of attraction of stable equilibria (at least at relatively low speed). A negative effect is that, even for straight running, the driver seem introducing a subcritical Hopf bifurcation which limits the maximum forward speed of some understeering vehicles (that could run faster with fixed steering wheel). Both the mentioned positive and negative effects appear to be applicable to autonomous vehicles as well. Further studies could be useful to overcome the limitations on the stability of current autonomous vehicles that have been identified in the present research.

Emergency obstacle avoidance trajectory tracking control based on active disturbance rejection for autonomous vehicles

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092110
Author(s):  
Runqiao Liu ◽  
Minxiang Wei ◽  
Nan Sang
Keyword(s):  
Obstacle Avoidance ◽  
Disturbance Rejection ◽  
Autonomous Vehicles ◽  
High Speed ◽  
Autonomous Vehicle ◽  
Angular Frequency ◽  
Vehicle Speed ◽  
Feed Forward ◽  
Yaw Rate ◽  
Active Disturbance Rejection

To solve the problem of understeer and oversteer for autonomous vehicle under high-speed emergency obstacle avoidance conditions, considering the effect of steering angular frequency and vehicle speed on yaw rate for four-wheel steering vehicles in the frequency domain, a feed-forward controller for four-wheel steering autonomous vehicles that tracks the desired yaw rate is proposed. Furthermore, the steering sensitivity coefficient of the vehicle is compensated linearly with the change in the steering angular frequency and vehicle speed. In addition, to minimize the tracking errors caused by vehicle nonlinearity and external disturbances, an active disturbance rejection control feedback controller that tracks the desired lateral displacement and desired yaw angle is designed. Finally, CarSim® obstacle avoidance simulation results show that an autonomous vehicle with the four-wheel steering path tracking controller consisting of feed-forward control and feedback control could not only improve the tire lateral forces but also reduce tail flicking (oversteer) and pushing ahead (understeer) under high-speed emergency obstacle avoidance conditions.

scholarly journals CAPformer: Pedestrian Crossing Action Prediction Using Transformer

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5694
Author(s):  
Javier Lorenzo ◽  
Ignacio Parra Alonso ◽  
Rubén Izquierdo ◽  
Augusto Luis Ballardini ◽  
Álvaro Hernández Saz ◽  
...  
Keyword(s):  
Language Processing ◽  
Autonomous Vehicles ◽  
State Of The Art ◽  
Image Data ◽  
Vehicle Speed ◽  
Action Prediction ◽  
Kinematic Data ◽  
Proposed Model ◽  
Temporal Models ◽  
Pedestrian Crossing

Anticipating pedestrian crossing behavior in urban scenarios is a challenging task for autonomous vehicles. Early this year, a benchmark comprising JAAD and PIE datasets have been released. In the benchmark, several state-of-the-art methods have been ranked. However, most of the ranked temporal models rely on recurrent architectures. In our case, we propose, as far as we are concerned, the first self-attention alternative, based on transformer architecture, which has had enormous success in natural language processing (NLP) and recently in computer vision. Our architecture is composed of various branches which fuse video and kinematic data. The video branch is based on two possible architectures: RubiksNet and TimeSformer. The kinematic branch is based on different configurations of transformer encoder. Several experiments have been performed mainly focusing on pre-processing input data, highlighting problems with two kinematic data sources: pose keypoints and ego-vehicle speed. Our proposed model results are comparable to PCPA, the best performing model in the benchmark reaching an F1 Score of nearly 0.78 against 0.77. Furthermore, by using only bounding box coordinates and image data, our model surpasses PCPA by a larger margin (F1=0.75 vs. F1=0.72). Our model has proven to be a valid alternative to recurrent architectures, providing advantages such as parallelization and whole sequence processing, learning relationships between samples not possible with recurrent architectures.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

Robust steering assistance control for tracking large-curvature path considering uncertainties of driver’s steering behavior

2021 ◽  
pp. 095440702097682
Author(s):  
Jinxiang Wang ◽  
Zhenwu Fang ◽  
Mengmeng Dai ◽  
Guodong Yin ◽  
Jingjing Xia ◽  
...  
Keyword(s):  
Path Following ◽  
Vehicle Speed ◽  
Time Varying ◽  
Steering Control ◽  
Characteristic Parameters ◽  
Parameter Uncertainties ◽  
Large Curvature ◽  
Steering Characteristics ◽  
Dynamic Compensator ◽  
Takagi Sugeno

A human-machine shared steering control is presented in this paper for tracking large-curvature path, considering uncertainties of driver’s steering characteristics. A driver-vehicle-road (DVR) model is proposed in which uncertain characteristic parameters are defined to describe the human driver’s steering behaviors in tracking large-curvature path. Then the radial basis function neural network (RBF) is used to estimate parameters of different drivers’ characteristics and to obtain the boundaries of these parameters. Parameter uncertainties of the driver’s steering characteristics and time-varying vehicle speed of the DVR model are handled with the Takagi-Sugeno (T-S) fuzzy logic. And these parameter uncertainties are considered in the design of the shared steering controller. Then based on the DVR model, a T-S fuzzy full-order dynamic compensator with D-pole assignment is designed to assist driver’s steering for tracking path with large curvature. Simulation results show that the proposed controller can provide individual levels of steering assistance in path following according to driver’s proficiency, and can improve driving comfort significantly.

Virtual Reality, Augmented Reality, and Virtual Environments: Demonstrations of Current Technologies and Future Directions

2020 ◽  
Vol 64 (1) ◽  
pp. 2119-2123
Author(s):  
Sarah Beadle ◽  
Randall Spain ◽  
Benjamin Goldberg ◽  
Mahdi Ebnali ◽  
Shannon Bailey ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
Human Factors ◽  
Virtual Environments ◽  
Medical Training ◽  
Risk Environment ◽  
Future Directions ◽  
Simulated Environments ◽  
Interactive Session ◽  
Immersive Technologies

Virtual environments and immersive technologies are growing in popularity for human factors purposes. Whether it is training in a low-risk environment or using simulated environments for testing future automated vehicles, virtual environments show promise for the future of our field. The purpose of this session is to have current human factors practitioners and researchers demonstrate their immersive technologies. This is the eighth iteration of the “Me and My VE” interactive session. Presenters in this session will provide a brief introduction of their virtual reality, augmented reality, or virtual environment work before engaging with attendees in an interactive demonstration period. During this period, the presenters will each have a multimedia display of their immersive technology as well as discuss their work and development efforts. The selected demonstrations cover issues of designing immersive interfaces, military and medical training, and using simulation to better understand complex tasks. This includes a mix of government, industry, and academic-based work. Attendees will be virtually immersed in the technologies and research presented allowing for interaction with the work being done in this field.

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