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 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 Deep reinforcement learning based control for Autonomous Vehicles in CARLA

2022 ◽  
Author(s):  
Óscar Pérez-Gil ◽  
Rafael Barea ◽  
Elena López-Guillén ◽  
Luis M. Bergasa ◽  
Carlos Gómez-Huélamo ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Data Sources ◽  
Simulation Environment ◽  
Urban Simulation ◽  
Policy Gradient ◽  
Almost All ◽  
Control Layer

AbstractNowadays, Artificial Intelligence (AI) is growing by leaps and bounds in almost all fields of technology, and Autonomous Vehicles (AV) research is one more of them. This paper proposes the using of algorithms based on Deep Learning (DL) in the control layer of an autonomous vehicle. More specifically, Deep Reinforcement Learning (DRL) algorithms such as Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) are implemented in order to compare results between them. The aim of this work is to obtain a trained model, applying a DRL algorithm, able of sending control commands to the vehicle to navigate properly and efficiently following a determined route. In addition, for each of the algorithms, several agents are presented as a solution, so that each of these agents uses different data sources to achieve the vehicle control commands. For this purpose, an open-source simulator such as CARLA is used, providing to the system with the ability to perform a multitude of tests without any risk into an hyper-realistic urban simulation environment, something that is unthinkable in the real world. The results obtained show that both DQN and DDPG reach the goal, but DDPG obtains a better performance. DDPG perfoms trajectories very similar to classic controller as LQR. In both cases RMSE is lower than 0.1m following trajectories with a range 180-700m. To conclude, some conclusions and future works are commented.

scholarly journals Driver Take-Over Reaction in Autonomous Vehicles with Rotatable Seats

Safety ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 34
Author(s):  
Shi Cao ◽  
Pinyan Tang ◽  
Xu Sun
Keyword(s):  
Reaction Time ◽  
Interior Design ◽  
Situation Awareness ◽  
Repeated Measures ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Maximum Magnitude ◽  
Significant Difference ◽  
Driving Conditions

A new concept in the interior design of autonomous vehicles is rotatable or swivelling seats that allow people sitting in the front row to rotate their seats and face backwards. In the current study, we used a take-over request task conducted in a fixed-based driving simulator to compare two conditions, driver front-facing and rear-facing. Thirty-six adult drivers participated in the experiment using a within-subject design with take-over time budget varied. Take-over reaction time, remaining action time, crash, situation awareness and trust in automation were measured. Repeated measures ANOVA and Generalized Linear Mixed Model were conducted to analyze the results. The results showed that the rear-facing configuration led to longer take-over reaction time (on average 1.56 s longer than front-facing, p < 0.001), but it caused drivers to intervene faster after they turned back their seat in comparison to the traditional front-facing configuration. Situation awareness in both front-facing and rear-facing autonomous driving conditions were significantly lower (p < 0.001) than the manual driving condition, but there was no significant difference between the two autonomous driving conditions (p = 1.000). There was no significant difference of automation trust between front-facing and rear-facing conditions (p = 0.166). The current study showed that in a fixed-based simulator representing a conditionally autonomous car, when using the rear-facing driver seat configuration (where participants rotated the seat by themselves), participants had longer take-over reaction time overall due to physical turning, but they intervened faster after they turned back their seat for take-over response in comparison to the traditional front-facing seat configuration. This behavioral change might be at the cost of reduced take-over response quality. Crash rate was not significantly different in the current laboratory study (overall the average rate of crash was 11%). A limitation of the current study is that the driving simulator does not support other measures of take-over request (TOR) quality such as minimal time to collision and maximum magnitude of acceleration. Based on the current study, future studies are needed to further examine the effect of rotatable seat configurations with more detailed analysis of both TOR speed and quality measures as well as in real world driving conditions for better understanding of their safety implications.

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 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.

Blockchain Security Using Secure Multi-Party Computation

2021 ◽  
pp. 178-195
Author(s):  
Jenila Livingston L. M. ◽  
Ashutosh Satapathy ◽  
Agnel Livingston L. G. X. ◽  
Merlin Livingston L. M.
Keyword(s):  
Autonomous Vehicles ◽  
Cooperative Control ◽  
Personal Information ◽  
Autonomous Vehicle ◽  
Privacy Preserving ◽  
Human Interaction ◽  
Security And Privacy ◽  
Blockchain Technology ◽  
Vehicle Data ◽  
Real Model

In secure multi-party computation (SMC), multiple distributed parties jointly carry out the computation over their confidential data without compromising data security and privacy. It is a new emerging cryptographic technique used in huge applications such as electronic auction bidding, electronic voting, protecting personal information, secure transaction processing, privacy preserving data mining, and privacy preserving cooperative control of connected autonomous vehicles. This chapter presents two model paradigms of SMC (i.e., ideal model prototype and real model prototype). It also deals with the type and applications of adversaries, properties, and the techniques of SMC. The three prime types of SMC techniques such as randomization, cryptographic techniques using oblivious transfer, and anonymization methods are discussed and illustrated by protective procedures with suitable examples. Finally, autonomous vehicle interaction leveraged with blockchain technology to store and use vehicle data without any human interaction is also discussed.

A value-based scheduling approach for real-time autonomous vehicle control

Robotica ◽  
2000 ◽  
Vol 18 (3) ◽  
pp. 273-279 ◽  
Author(s):  
D. Prasad ◽  
A. Burns
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Measurement Theory ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Optimal Choice ◽  
A Value ◽  
Run Time ◽  
System States ◽  
Time Systems

In future real-time systems such as those required for intelligent autonomous vehicle control, we need flexibility in choosing the set of services to support under varying environmental conditions and system states. It is not feasible to make an optimal choice of services at run-time, so we propose a method of ranking the services pre-run-time, based on the ‘utility' of each service. This paper focuses on the problem of calculating a ‘value' for the utility of each service alternative. We show how to derive values systematically and rationally, using Measurement Theory and Decision Analysis. The approach relies on engineering judgement and data input by a domain expert. In the context of autonomous vehicles, we believe that such knowledge would be available, making ‘value-based scheduling' a feasible approach.

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.

Assuring Fully Autonomous Vehicles Safety by Design: The Autonomous Vehicle Control (AVC) Module Strategy

2017 ◽  
Author(s):  
Caroline Bianca Santos Tancredi Molina ◽  
Jorge Rady de Almeida ◽  
Lucio F. Vismari ◽  
Rodrigo Ignacio R. Gonzalez ◽  
Jamil K. Naufal ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Control

Explicit Path Tracking by Autonomous Vehicles

Robotica ◽  
1992 ◽  
Vol 10 (6) ◽  
pp. 539-554 ◽  
Author(s):  
Dong Hun Shin ◽  
Sanjiv Singh ◽  
Ju Jang Lee
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Obstacle Detection ◽  
Experimental Results ◽  
Order Effects ◽  
Dynamical Models ◽  
First Order ◽  
Novel Approach ◽  
Kinematic Models

SUMMARYWe have suggested a novel approach to autonomously navigate a full sized autonomous vehicle that separately treats vehicle control and obstacle detection. In this paper we discuss the vehicle control that has enabled our autonomous vehicle to travel at speeds upto 20mph. We point out the limitations of existing schemes that restrict their consideration to kinematic models and show that it is possible to obtain an increase in performance through the use of approximate dynamical models that capture first–order effects. Our approach combines such a modeling philosophy with accurate feedback in world coordinates from sensors that have only recently become available. Experimental results of our implementation on NavLab, a modified van at CMU, are presented.

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