اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدModeling Ice Friction for Vehicle Dynamics of a Bobsled with Application in Driver Evaluation and Driving Simulation
181
0
0.0
(
0
)
نشر من قبل
Julian Von Schleinitz
تاريخ النشر
2021
مجال البحث
الهندسة المعلوماتية
والبحث باللغة
English
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We provide an ice friction model for vehicle dynamics of a two-man bobsled which can be used for driver evaluation and in a driver-in-the-loop simulator. Longitudinal friction is modeled by combining experimental results with finite element simulations to yield a correlation between contact pressure and friction. To model lateral friction, we collect data from 44 bobsleigh runs using special sensors. Non-linear regression is used to fit a bob-specific one-track vehicle dynamics model to the data. It is applied in driving simulation and enables a novel method for bob driver evaluation. Bob drivers with various levels of experience are investigated. It shows that a similar performance of the top drivers results from different driving styles.
قيم البحث
اقرأ أيضاً
Trust in automation, or more recently trust in autonomy, has received extensive research attention in the past two decades. The majority of prior literature adopted a snapshot view of trust and typically evaluated trust through questionnaires adminis
tered at the end of an experiment. This snapshot view, however, does not acknowledge that trust is a time-variant variable that can strengthen or decay over time. To fill the research gap, the present study aims to model trust dynamics when a human interacts with a robotic agent over time. The underlying premise of the study is that by interacting with a robotic agent and observing its performance over time, a rational human agent will update his/her trust in the robotic agent accordingly. Based on this premise, we develop a personalized trust prediction model based on Beta distribution and learn its parameters using Bayesian inference. Our proposed model adheres to three major properties of trust dynamics reported in prior empirical studies. We tested the proposed method using an existing dataset involving 39 human participants interacting with four drones in a simulated surveillance mission. The proposed method obtained a Root Mean Square Error (RMSE) of 0.072, significantly outperforming existing prediction methods. Moreover, we identified three distinctive types of trust dynamics, the Bayesian decision maker, the oscillator, and the disbeliever, respectively. This prediction model can be used for the design of individualized and adaptive technologies.
Recent developments in advanced driving assistance systems (ADAS) that rely on some level of autonomy have led the automobile industry and research community to investigate the impact they might have on driving performance. However, most of the resea
rch performed so far is based on simulated environments. In this study, we investigated the behavior of drivers in a vehicle with automated driving system (ADS) capabilities in a real-life driving scenario. We analyzed their response to a take over request (TOR) at two different driving speeds while being engaged in non-driving-related tasks (NDRT). Results from the performed experiments showed that driver reaction time to a TOR, gaze behavior and self-reported trust in automation were affected by the type of NDRT being concurrently performed and driver reaction time and gaze behavior additionally depended on the driving or vehicle speed at the time of TOR.
Automated driving system - dedicated vehicles (ADS-DVs), specially designed for people with various disabilities, can be beneficial to improve their mobility. However, research related to autonomous vehicles (AVs) for people with cognitive disabiliti
es, especially Autism Spectrum Disorder (ASD) is limited. Thus, in this study, we focused on the challenge that we framed: How might we design an ADS-DV that benefits people with ASD and their caregivers?. In order to address the design challenge, we followed the human-centered design process. First, we conducted user research with caregivers of people with ASD. Second, we identified their user needs, including safety, monitoring and updates, individual preferences, comfort, trust, and reliability. Third, we generated a large number of ideas with brainstorming and affinity diagrams, based on which we proposed an ADS-DV prototype with a mobile application and an interior design. Fourth, we tested both the low-fidelity and high-fidelity prototypes to fix the possible issues. Our preliminary results showed that such an ASD-DV would potentially improve the mobility of those with ASD without worries.
This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios. This research takes inspiration from a large-field looming sensitive neuron,
i.e., the lobula giant movement detector (LGMD) in the locusts visual pathways, which represents high spike frequency to rapid approaching objects. Building upon our previous models, in this paper we propose a novel inhibition mechanism that is capable of adapting to different levels of background complexity. This adaptive mechanism works effectively to mediate the local inhibition strength and tune the temporal latency of local excitation reaching the LGMD neuron. As a result, the proposed model is effective to extract colliding cues from complex dynamic visual scenes. We tested the proposed method using a range of stimuli including simulated movements in grating backgrounds and shifting of a natural panoramic scene, as well as vehicle crash video sequences. The experimental results demonstrate the proposed method is feasible for fast collision perception in real-world situations with potential applications in future autonomous vehicles.
Surgeons must accomplish complex technical and intellectual tasks that can generate unexpected and serious challenges with little or no room for error. In the last decade, computer simulations have played an increasing role in surgical training, pre-
operative planning, and biomedical research. Specifically, visuo-haptic simulations have been the focus of research to develop advanced e-Learning systems facilitating surgical training. The cost of haptic hardware was reduced through mass scale production and as haptics gained popularity in the gaming industry. Visuo-haptic simulations combine the tactile sense with visual information and provide training scenarios with a high degree of reality. For surgical training, such scenarios can be used as ways to gain, improve, and assess resident and expert surgeons skills and knowledge.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
