6,431 research outputs found
Sensation seeking, non-contextual decision making, and driving abilities as measured through a moped simulator.
The general aim of the present study was to explore the relations between driving style (assessed through a moped riding simulator) and psychological variables such as sensation seeking and decision making. Because the influences of sensation seeking and decision making on driving styles have been studied separately in the literature, we have tried to investigate their mutual relations so as to include them in a more integrated framework. Participants rode the Honda Riding Trainer (HRT) simulator, filled in the Sensation Seeking Scale V (SSS V), and performed the Iowa Gambling Task (IGT). A cluster analysis of the HRT riding indexes identified three groups: Prudent, Imprudent, and Insecure riders. First, the results showed that Insecure males seek thrills and adventure less than both Prudent males and Insecure females, whereas Prudent females are less disinhibited than both Prudent males and Insecure females. Moreover, concerning the relations among SSS, decision making as measured by the IGT, and riding performance, high thrill and adventure seekers performed worse in the simulator only if they were also bad decision makers, indicating that these two traits jointly contribute to the quality of riding performance. From an applied perspective, these results also provide useful information for the development of protocols for assessing driving abilities among novice road users. Indeed, the relation between risk proneness and riding style may allow for the identification of road-user populations who require specific training
A Real-Time Remote IDS Testbed for Connected Vehicles
Connected vehicles are becoming commonplace. A constant connection between
vehicles and a central server enables new features and services. This added
connectivity raises the likelihood of exposure to attackers and risks
unauthorized access. A possible countermeasure to this issue are intrusion
detection systems (IDS), which aim at detecting these intrusions during or
after their occurrence. The problem with IDS is the large variety of possible
approaches with no sensible option for comparing them. Our contribution to this
problem comprises the conceptualization and implementation of a testbed for an
automotive real-world scenario. That amounts to a server-side IDS detecting
intrusions into vehicles remotely. To verify the validity of our approach, we
evaluate the testbed from multiple perspectives, including its fitness for
purpose and the quality of the data it generates. Our evaluation shows that the
testbed makes the effective assessment of various IDS possible. It solves
multiple problems of existing approaches, including class imbalance.
Additionally, it enables reproducibility and generating data of varying
detection difficulties. This allows for comprehensive evaluation of real-time,
remote IDS.Comment: Peer-reviewed version accepted for publication in the proceedings of
the 34th ACM/SIGAPP Symposium On Applied Computing (SAC'19
Evolving Models From Observed Human Performance
To create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behavior models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human performance within simulated agents, with Genetic Programming, a machine learning algorithm to construct the behavior knowledge in accordance to the paradigm. This synergistic combination of well-documented AI methodologies has resulted in a new algorithm that effectively and automatically builds simulated agents with human behavior. This algorithm was tested extensively with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents show not only the ability/capability to automatically learn and generalize the behavior of the human observed, but they also capture some of the personal behavior patterns observed among the five humans. Furthermore, the agents exhibited a performance that was at least as good as agents developed manually by a knowledgeable engineer
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Diversity of environments is a key challenge that causes learned robotic
controllers to fail due to the discrepancies between the training and
evaluation conditions. Training from demonstrations in various conditions can
mitigate---but not completely prevent---such failures. Learned controllers such
as neural networks typically do not have a notion of uncertainty that allows to
diagnose an offset between training and testing conditions, and potentially
intervene. In this work, we propose to use Bayesian Neural Networks, which have
such a notion of uncertainty. We show that uncertainty can be leveraged to
consistently detect situations in high-dimensional simulated and real robotic
domains in which the performance of the learned controller would be sub-par.
Also, we show that such an uncertainty based solution allows making an informed
decision about when to invoke a fallback strategy. One fallback strategy is to
request more data. We empirically show that providing data only when requested
results in increased data-efficiency.Comment: Copyright 20XX IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
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this work in other work
Meta Adaptation using Importance Weighted Demonstrations
Imitation learning has gained immense popularity because of its high
sample-efficiency. However, in real-world scenarios, where the trajectory
distribution of most of the tasks dynamically shifts, model fitting on
continuously aggregated data alone would be futile. In some cases, the
distribution shifts, so much, that it is difficult for an agent to infer the
new task. We propose a novel algorithm to generalize on any related task by
leveraging prior knowledge on a set of specific tasks, which involves assigning
importance weights to each past demonstration. We show experiments where the
robot is trained from a diversity of environmental tasks and is also able to
adapt to an unseen environment, using few-shot learning. We also developed a
prototype robot system to test our approach on the task of visual navigation,
and experimental results obtained were able to confirm these suppositions
The Effect of Expertise during Simulated Flight Emergencies on the Autonomic Response and Operative Performance in Military Pilots.
Heart rate variability (HRV) and performance response during emergency flight maneuvers were analyzed. Two expert pilots (ages 35 and 33) and two rookie pilots (ages 25) from the Portuguese Air Force participated in this case–control report study. Participants had to complete the following emergency protocols in a flight simulator: (1) take-off engine failure, (2) flight engine failure close to the base, (3) flight engine failure far away from the base, and (4) alternator failure. The HRV was collected during all these maneuvers, as well as the performance data (the time it took to go through the emergency protocol and the subjective information from the flight simulator operator). Results regarding autonomic modulation showed a higher sympathetic response during the emergency maneuvers when compared to baseline. In some cases, there was also a higher sympathetic response during the emergency maneuvers when compared with the take-off protocol. Regarding performance data, the expert pilots accomplished the missions in less time than the rookie pilots. Autonomic modulation measured from HRV through portable devices can easily relay important information. This information is relevant since characterizing these maneuvers can provide helpful information to design training strategies to improve those psychophysiological responses
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