Toward More Realistic Driving Behavior Models for Autonomous Vehicles in Driving Simulators

2003 ◽  
Vol 1843 (1) ◽  
pp. 41-49 ◽  
Author(s):  
Talal Al-Shihabi ◽  
Ronald R. Mourant
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Three Dimensional ◽  
Driving Behavior ◽  
Behavioral Models ◽  
Aggressive Driving ◽  
Decision Making Unit ◽  
Driving Model ◽  
Dynamics Models

Autonomous vehicles are perhaps the most encountered element in a driving simulator. Their effect on the realism of the simulator is critical. For autonomous vehicles to contribute positively to the realism of the hosting driving simulator, they need to have a realistic appearance and, possibly more importantly, realistic behavior. Addressed is the problem of modeling realistic and humanlike behaviors on simulated highway systems by developing an abstract framework that captures the details of human driving at the microscopic level. This framework consists of four units that together define and specify the elements needed for a concrete humanlike driving model to be implemented within a driving simulator. These units are the perception unit, the emotions unit, the decision-making unit, and the decision-implementation unit. Realistic models of humanlike driving behavior can be built by implementing the specifications set by the driving framework. Four humanlike driving models have been implemented on the basis of the driving framework: ( a) a generic normal driving model, ( b) an aggressive driving model, ( c) an alcoholic driving model, and ( d) an elderly driving model. These driving models provide experiment designers with a powerful tool for generating complex traffic scenarios in their experiments. These behavioral models were incorporated along with three-dimensional visual models and vehicle dynamics models into one entity, which is the autonomous vehicle. Subjects perceived the autonomous vehicles with the described behavioral models as having a positive effect on the realism of the driving simulator. The erratic driving models were identified correctly by the subjects in most cases.

scholarly journals Transporting Children in Autonomous Vehicles: An Exploratory Study

2019 ◽  
Vol 62 (2) ◽  
pp. 278-287 ◽  
Author(s):  
Patrice D. Tremoulet ◽  
Thomas Seacrist ◽  
Chelsea Ward McIntosh ◽  
Helen Loeb ◽  
Anna DiPietro ◽  
...  
Keyword(s):  
Extracurricular Activities ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Remote Access ◽  
Unaccompanied Children ◽  
Potential Applications ◽  
Minimum Age ◽  
Audio Communication ◽  
Vehicle Information

Objective Identify factors that impact parents’ decisions about allowing an unaccompanied child to ride in an autonomous vehicle (AV). Background AVs are being tested in several U.S. cities and on highways in multiple states. Meanwhile, suburban parents are using ridesharing services to shuttle children from school to extracurricular activities. Parents may soon be able to hire AVs to transport children. Method Nineteen parents of 8- to 16-year-old children, and some of their children, rode in a driving simulator in autonomous mode, then were interviewed. Parents also participated in focus groups. Topics included minimum age for solo child passengers, types of trips unaccompanied children might take, and vehicle features needed to support child passengers. Results Parents would require two-way audio communication and prefer video feeds of vehicle interiors, seatbelt checks, automatic locking, secure passenger identification, and remote access to vehicle information. Parents cited convenience as the greatest benefit and fear that AVs could not protect passengers during unplanned trip interruptions as their greatest concern. Conclusion Manufacturers have an opportunity to design family-friendly AVs from the outset, rather than retrofit them to be safe for child passengers. More research, especially usability studies where families interact with technology prototypes, is needed to understand how AV design impacts child passengers. Application Potential applications of this research include not only designing vehicles that can be used to safely transport children, seniors who no longer drive, and individuals with disabilities but also developing regulations, policies, and societal infrastructure to support safe child transport via AVs.

scholarly journals Common Driving Notification Protocol Based on Classified Driving Behavior for Cooperation Intelligent Autonomous Vehicle Using Vehicular Ad-Hoc Network Technology

2015 ◽  
Vol 5 (1) ◽  
pp. 5-21 ◽  
Author(s):  
Che-Hung Lin ◽  
Fang-Yan Dong ◽  
Kaoru Hirota
Keyword(s):  
Autonomous Vehicles ◽  
Ad Hoc ◽  
Autonomous Vehicle ◽  
Driving Behavior ◽  
Packet Transmission ◽  
Wireless Data ◽  
Standard Format ◽  
Simulation Tools ◽  
Identification Time ◽  
Basic Protocol

Abstract A protocol, called common driving notification protocol (CDNP), is proposed based on the classified driving behavior for intelligent autonomous vehicles, and it defines a standard with common messages and format for vehicles. The common standard format and definitions of CDNP packet make the autonomous vehicles have a common language to exchange more detail driving decision information of various driving situations, decrease the identification time for one vehicle to identify the driving decisions of other vehicles before or after those driving decisions are performed. The simulation tools, including NS- 3 and SUMO, are used to simulate the wireless data packet transmission and the vehicle mobility; the experiment results present that the proposed protocol, CDNP, can increase the reaction preparing time with maximum value 250 seconds, decrease the identification time and the average travel time. Prospectively, it is decided to implement the CDNP as a protocol stack in the Linux kernel to provide the basic protocol capability for real world transmission testing.

scholarly journals An Investigation of Autonomous Vehicle Roundabout Situation

2019 ◽  
Vol 48 (3) ◽  
pp. 236-241
Author(s):  
Hang Cao ◽  
Máté Zöldy
Keyword(s):  
Fuel Consumption ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Preliminary Test ◽  
Driving Behavior ◽  
Physical Parameters ◽  
Test Scenario ◽  
Vehicle Type ◽  
Fuel Consumption And Emissions ◽  
The Impact

The aim of this paper is to evaluate the impact of connected autonomous behavior in real vehicles on vehicle fuel consumption and emission reductions. Authors provide a preliminary theoretical summary to assess the driving conditions of autonomous vehicles in roundabout, which attempts exploring the impact of driving behavior patterns on fuel consumption and emissions, and including other key factors of autonomous vehicles to reduce fuel consumption and emissions. After summarizing, driving behavior, effective in-vehicle systems, both roundabout physical parameters and vehicle type are all play an important role in energy using. ZalaZONE’s roundabout is selected for preliminary test scenario establishment, which lays a design foundation for further in-depth testing.

scholarly journals An Integrated Model for User State Detection of Subjective Discomfort in Autonomous Vehicles

Vehicles ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 764-777
Author(s):  
Dario Niermann ◽  
Alexander Trende ◽  
Klas Ihme ◽  
Uwe Drewitz ◽  
Cornelia Hollander ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Linear Models ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
The Road ◽  
Novel Approach ◽  
Driving Scenario ◽  
On The Road ◽  
Using Data ◽  
Subjective Discomfort

The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling of discomfort of human passengers while driving autonomously and the automatic detection of this discomfort with several model approaches, using the combination of different data sources. Based on a driving simulator study, we analyzed the discomfort reports of 50 participants for autonomous inner city driving. We found that perceived discomfort depends on the driving scenario (with discomfort generally peaking in complex situations) and on the passenger (resulting in interindividual differences in reported discomfort extend and duration). Further, we describe three different model approaches on how to predict the passenger discomfort using data from the vehicle’s sensors as well as physiological and behavioral data from the passenger. The model’s precision varies greatly across the approaches, the best approach having a precision of up to 80%. All of our presented model approaches use combinations of linear models and are thus fast, transparent, and safe. Lastly, we analyzed these models using the SHAP method, which enables explaining the models’ discomfort predictions. These explanations are used to infer the importance of our collected features and to create a scenario-based discomfort analysis. Our work demonstrates a novel approach on passenger state modelling with simple, safe, and transparent models and with explainable model predictions, which can be used to adapt the vehicles’ actions to the needs of the passenger.

Putting the Brakes on Autonomous Vehicle Control

2016 ◽  
Vol 60 (1) ◽  
pp. 1859-1863 ◽  
Author(s):  
Kelly Funkhouser ◽  
Frank Drews
Keyword(s):  
Reaction Time ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Cognitive Task ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Human Interaction ◽  
Baseline Scenario ◽  
Mental Demand ◽  
Positive Linear Correlation

The assimilation of automation in commuter vehicles is rapidly increasing, as too are the concerns with these technologies. Human interaction with autonomous vehicles must be thoroughly researched to understand the quantification and qualification of interactive behaviors with these systems. We developed a study using a high-fidelity driving simulator to mimic probable breakdowns with these systems to better understand the subsequent human responses and to explore the necessary technological requirements to overcome potential problems. 30 participants engaged in a driving scenario switching between manual and autonomous vehicle control. We accounted for individual differences in braking reaction time while simultaneously engaging in a secondary cognitive task during times of autonomous vehicle control. Results show the average RT for baseline scenarios without the cognitive task was 832.1 milliseconds while the average RT for baseline scenarios with the cognitive task was 908.4 milliseconds; a 9.17% significant increase. The average RT for the autonomous scenario was 1357.0 milliseconds; a significant increase of 49.38% over the baseline scenario with the cognitive task that can be attributed to the addition of automation. We found a positive linear correlation of time spent in autonomous control and subsequent braking reaction time. Additionally, cognitive task difficulty, attention allocation, self-reported mental demand, fatigue, and heart rate affect reaction time when cued to take control of the vehicle.

scholarly journals Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Weilong Song ◽  
Guangming Xiong ◽  
Huiyan Chen
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Transition Process ◽  
Autonomous Driving ◽  
Driving Safety ◽  
Time Step ◽  
Reward Function ◽  
Pass Through

Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging problem in an uncontrolled intersection scenario. In order to consider uncertain intentions, we first develop a continuous hidden Markov model to predict both the high-level motion intention (e.g., turn right, turn left, and go straight) and the low level interaction intentions (e.g., yield status for related vehicles). Then a partially observable Markov decision process (POMDP) is built to model the general decision-making framework. Due to the difficulty in solving POMDP, we use proper assumptions and approximations to simplify this problem. A human-like policy generation mechanism is used to generate the possible candidates. Human-driven vehicles’ future motion model is proposed to be applied in state transition process and the intention is updated during each prediction time step. The reward function, which considers the driving safety, traffic laws, time efficiency, and so forth, is designed to calculate the optimal policy. Finally, our method is evaluated in simulation with PreScan software and a driving simulator. The experiments show that our method could lead autonomous vehicle to pass through uncontrolled intersections safely and efficiently.

scholarly journals Three-Dimensional LiDAR Decoder Design for Autonomous Vehicles in Smart Cities

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Yu-Cheng Fan ◽  
Sheng-Bi Wang
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Three Dimensional ◽  
Original Data ◽  
Depth Information ◽  
Learning Technology ◽  
Cloud Data ◽  
Self Driving Cars

With the advancement of artificial intelligence, deep learning technology is applied in many fields. The autonomous car system is one of the most important application areas of artificial intelligence. LiDAR (Light Detection and Ranging) is one of the most critical components of self-driving cars. LiDAR can quickly scan the environment to obtain a large amount of high-precision three-dimensional depth information. Self-driving cars use LiDAR to reconstruct the three-dimensional environment. The autonomous car system can identify various situations in the vicinity through the information provided by LiDAR and choose a safer route. This paper is based on Velodyne HDL-64 LiDAR to decode data packets of LiDAR. The decoder we designed converts the information of the original data packet into X, Y, and Z point cloud data so that the autonomous vehicle can use the decoded information to reconstruct the three-dimensional environment and perform object detection and object classification. In order to prove the performance of the proposed LiDAR decoder, we use the standard original packets used for the comparison of experimental data, which are all taken from the Map GMU (George Mason University). The average decoding time of a frame is 7.678 milliseconds. Compared to other methods, the proposed LiDAR decoder has higher decoding speed and efficiency.

Investigating the Effects of Automated Driving Styles and Driver’s Driving Styles on Driver Trust, Acceptance, and Take Over Behaviors

2020 ◽  
Vol 64 (1) ◽  
pp. 2001-2005
Author(s):  
Zheng Ma ◽  
Yiqi Zhang
Keyword(s):  
Individual Differences ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Interaction Effects ◽  
Aggressive Driving ◽  
Automated Driving ◽  
Driver Safety ◽  
Driving Style ◽  
The Impact

Autonomous Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, driver could be reluctant to ride with AVs due to the lack of trust and acceptance of AV’s driving styles. The present study investigated the impact of driver’s driving style (aggressive/defensive) and the designed driving styles of AVs (aggressive/defensive) on driver’s trust, acceptance, and take-over behavior in fully autonomous vehicles. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve scenarios in either an aggressive AV or a defensive AV. Results revealed that drivers’ trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV’s driving style and driver’s driving style. The findings implied that driver’s individual differences should be considered in the design of AV’s driving styles to enhance driver’s trust and acceptance of AVs and reduce undesired take over behaviors.

scholarly journals Learning-Based Stochastic Driving Model for Autonomous Vehicle Testing

2021 ◽  
pp. 036119812110357
Author(s):  
Lin Liu ◽  
Shuo Feng ◽  
Yiheng Feng ◽  
Xichan Zhu ◽  
Henry X. Liu
Keyword(s):  
Autonomous Vehicles ◽  
Large Scale ◽  
Short Term Memory ◽  
Autonomous Vehicle ◽  
Deterministic Models ◽  
Pilot Model ◽  
Proposed Model ◽  
Driving Model ◽  
Naturalistic Driving ◽  
Testing And Evaluation

In the simulation-based testing and evaluation of autonomous vehicles (AVs), how background vehicles (BVs) drive directly influences the AV’s driving behavior and further affects the test results. Most existing simulation platforms use either predetermined trajectories or deterministic driving models to model BV behaviors. However, predetermined BV trajectories cannot react to AV maneuvers, and deterministic models are different from real human drivers because of the lack of stochastic components and errors. Both methods lead to unrealistic traffic scenarios. This paper presents a learning-based stochastic driving model that meets the unique needs of AV testing (i.e., interactive and human-like stochasticity). The model is built based on the long short-term memory architecture. By incorporating the concept of quantile regression into the loss function of the model, the stochastic behaviors are reproduced without prior assumption of human drivers. The model is trained with the large-scale naturalistic driving data (NDD) from the Safety Pilot Model Deployment project and compared with a stochastic intelligent driving model (IDM). Analysis of individual trajectories shows that the proposed model can reproduce more similar trajectories of human drivers than IDM. To validate the ability of the proposed model in generating a naturalistic driving environment, traffic simulation experiments are implemented. The results show that traffic flow parameters such as speed, range, and time headway distribution match closely with the NDD, which is of significant importance for AV testing and evaluation.

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