Gaze behavior is widely treated as a trainable component of high-performance driving, yet its causal role in performance remains unclear. We tested whether enforcing expert gaze patterns improves circuit driving performance in a simulator study with 60 participants assigned to free gaze, skilled-gaze guidance, or novice-gaze guidance. Gaze guidance replayed naturalistic gaze trajectories from actual skilled and novice drivers during training, followed by an unguided retention session. Despite clear compliance with gaze instructions during training, gaze guidance had no significant effect on lap time, steering or pedal smoothness, or lateral deviation from an optimal racing line, aside from a minimal per-corner difference. Performance improvements were attributable to practice and persisted independently of gaze condition. These findings suggest that gaze guidance alone is insufficient to improve high-performance driving, underscoring the need for training approaches that integrate it with additional instruction or feedback.

How can I improve my performance here?Figure 1: After completing a lap, the driver pauses the video at a specific segment and asks ApexTrainer for feedback.The system generates context-aware suggestions based on the referenced location.

Learning motor skills for sports or performance driving is often done with professional instruction from expert human teachers, whose availability is limited. Our goal is to enable automated teaching via a learned model that interacts with the student similar to a human teacher. However, training such automated teaching systems is limited by the availability of highquality annotated datasets of expert teacher and student interactions as they are difficult to collect at scale. To address this data scarcity problem, we propose an approach for training a coaching system for complex motor tasks such as high performance driving via a Multi-Task Imitation Learning (MTIL) paradigm. MTIL allows our model to learn robust representations by utilizing self-supervised training signals from more readily available non-interactive datasets of humans performing the task of interest. We validate our approach with (1) a semi-synthetic dataset created from real human driving trajectories, (2) a professional track driving instruction dataset, (3) a track-racing driving simulator human-subject study, and (4) a system demonstration on an instrumented car at a race track. Our experiments show that the right set of auxiliary machine learning tasks improves prediction of teaching instructions. Moreover, in the human subjects study, students exposed to the instructions from our teaching system improve their ability to stay within track limits, and show favorable perception of the model's interaction with them, in terms of usefulness and satisfaction.

Drifting, a skillful driving technique involving intentional traction loss and counter-steering, traditionally demands high-speed maneuvers under high-friction conditions, posing significant risks and fear for novices. Our study explores low-µ (low friction) emulation, simulating icy conditions to facilitate drift training at safer, lower speeds. This approach not only enhances safety and mitigates fear by reducing the required speed for drifting, but also extends the time for them to react. A between-group design was employed, comparing drift training outcomes between participants trained exclusively in higher-µ conditions (control group) and those who trained initially in lower-µ conditions before transitioning to higher-µ conditions (target group). The performance was assessed through the average distance of continuous sliding, along with subjective measures of motivation and workload. The results showed that the target group achieved greater slide distances in the retention session and reported higher scores on the positive intrinsic motivation factors, suggesting enhanced performance and engagement.

Should fully autonomous vehicles (FAVs) be designed inclusively and accessibly, independence will be transformed for millions of people experiencing transportation-limiting disabilities worldwide. Although FAVs hold promise to improve efficient transportation without intervention, a truly accessible experience must enable user input, for all people, in many driving scenarios (e.g., to alter a route or pull over during an emergency). Therefore, this paper explores desires for control in FAVs among (n=23) people who are blind and visually impaired. Results indicate strong support for control across a battery of driving tasks, as well as the need for multimodal information. These findings inspired the design and evaluation of a novel multisensory interface leveraging mid-air gestures, audio, and haptics. All participants successfully navigated driving scenarios using our gestural-audio interface, reporting high ease-of-use. Contributions include the first inclusively designed gesture set for FAV control and insight regarding supplemental haptic and audio cues.

Automated vehicle control beyond friction limits is an emerging technology that can further improve vehicle safety. This paper focuses on the driver's understanding of steering behavior during automated counter-steering. We hypothesize that: 1) motorsports fans or racing game players (GP) have a correct mental model of automated counter-steering, while 2) ordinary drivers (Non-GP) do not, and 3) Non-GP incorrectly estimate rotation direction as being always consistent between the steering wheel and vehicle. We first propose a method to examine the accuracy of a driver's steering mental model. The method measures the participants' ability to estimate steering wheel rotation from a video only showing vehicle rotation and vice versa. We compared the performance of GP and Non-GP using videos with and without counter-steering. The results only support hypothesis 2. This indicates that automated counter-steering likely confuses most drivers no matter their prior knowledge, which makes the improvement of driver-vehicle communication critical.

High-fidelity driving simulators can act as testbeds for designing in-vehicle interfaces or validating the safety of novel driver assistance features. In this system paper, we develop and validate the safety of a mixed reality driving simulator system that enables us to superimpose virtual objects and events into the view of participants engaging in real-world driving in unmodified vehicles. To this end, we have validated the mixed reality system for basic driver cockpit and low-speed driving tasks, comparing the use of the system with non-headset and with the headset driving conditions, to ensure that participants behave and perform similarly using this system as they would otherwise. This paper outlines the operational procedures and protocols for using such systems for cockpit tasks (like using the parking brake, reading the instrument panel, and turn signaling) as well as basic low-speed driving exercises (such as steering around corners, weaving around obstacles, and stopping at a fixed line) in ways that are safe, effective, and lead to accurate, repeatable data collection about behavioral responses in real-world driving tasks.

This study specifically examines wall see-through visualization for drivers at blind corners to prevent crossing collisions. We believe that realizing the desired effect with the simplest visualization is a key to building practical systems, although previous studies mainly targeted rich visualization as if the wall were actually transparent. We compared several visualization levels using qualitative and quantitative measures based on performance of the driver's collision estimation and the meaning assignment to visual stimuli. The results revealed that displaying only the direction of the obscured vehicle by a small circle is sufficient for collision estimation, although it was perceived as less informative. We also obtained a preliminary result indicating that the meaning assignment performance is significantly lower in a peripheral region of the driver's view. Although both collision estimation and meaning assignment performance are necessary for building an effective system, these results clarify that future studies must specifically examine the meaning assignment performance of the stimuli.

Privacy has been a central concern of ubiquitous (pervasive) computing. Although the boundary between privacy and publicity dynamically moves depending on the context in which the issue is considered, access control, which is one of the most fundamental functionality constituting ubiquitous computing, is required to support perfect privacy, that is, anonymity and unlinkability. This paper presents a concrete protocol for anonymous access control that supports compliance to the distributed trust management model introduced by Blaze et al, efficiency for continual verification and provable security. In addition, the protocol is based on a practical trust model that models the heterogeneous structure of trust in the real world. The model defines a service provider, a service appliance, users and a device that users carry or wear as independent players, and further assumes that trust between them is independently established only based on their arbitrary mutual agreements.

Nowadays many organizations and enterprises have a Web site to help accomplish several business goals from sales to customer support. A very important activity for today's managers is how to improve the experience of visitors in their Web site and make it more effective. Several techniques that use knowledge from visitors' browsing behavior have shown good results to perform such task. However, how to introduce semantics into Web usage mining techniques is still a challenge. This work presents a way to obtain a semantic classification of visitors' sessions using a conceptual mining process. We performed experiments in a real Web site

In this paper, we propose a novel technique to estimate joint ROM (range of motion) from Mocap (motion capture) data. For each joint, the probability density function of joint angle is estimated by mixture of Gaussian, and the angle space is divided into Voronoi cells which have a kernel of Gaussian at these centers. Then, the sets of the Voronoi cells in two different joints, which occur in the Mocap data, are used to approximate the ROM taking into account the change related to the angle of the other joint. As all of this estimation processes are performed on the simple ball joint model, we do not need to introduce any complex musculo-skeletal model. Our method can also estimate the ROMs even for the joints which have difficulty to measure the angle directly by physical measurement as well as the joints of limbs

Web systems have reached a very important role in today's business world. Every day organizations fight to keep their present clients and to gain new ones. In order to accomplish this goal it is very important to make precise changes in the Web site content. However, the development of these improvements is a complex and specialized task because of the nature of the Web data itself. We propose a novel approach to successfully make changes to improve the Web site content using text mining. We use a self organizing feature map (SOFM) to find the most relevant text content, and then we propose a reverse clustering analysis in order to extract the most significant pages of the whole Web site. The effectiveness of this method was experimentally tested in a real Web site.

The Internet has become a big battlefield where organizations are trying to keep their present clients and to gain new ones. Two important weapons that the organizations have are to make a good Web site design and to have a content interesting for the visitors. To improve the Web site content, many tools have been developed. However, it is hard to figure out how to apply these changes. Furthermore, in complex Web sites, this is a non trivial task. We propose a novel approach that helps to improve a Web site content using a SOFM and performing a reverse clustering analysis that allows us to gather the most representative Web pages from a Web site, using this small set of pages as a guideline of how these enhancements should be performed. The effectiveness of the method was tested in a real Web site.

The current trend of image retrieval is to incorporate the image visual features used in content based image retrieval (CBIR) and semantics annotations used in metadata based image retrieval to enhance retrieval performance. Because of the pervasive of consumer imaging devices, building personal digital photograph libraries became an increasingly interested domain. Personal digital photograph collections have specific characteristics compare to general purpose image databases. Hence, annotation architecture specially designed for that plays an important role in building an interoperable data repository for future indexing, browsing and retrieving purposes. We propose a MPEG-7 based multimedia content description architecture, Dozen Dimensional Digital Content (DDDC), which annotates multimedia data with twelve main attributes regarding its semantic representation. In addition, we also proposed a machine-understandable "spatial and temporal based ontology" representation for the above DDDC semantics description to enable semiautomatic annotation process

We propose a fuzzy clustering method that relies on an artificial neural network scheme based on an encoder-decoder architecture with autoassociative training. The encoder is designed to implement a set of competing fuzzy membership functions which are trained to fit the data so that the decoder reconstruction error is minimized. In order to enforce a suitable cluster partitioning and membership distribution, the critical factor of the method is an entropy based regularization that constrains the encoder outputs. We present the results of our approach applied to synthetic data sets featuring both disjoin and intersecting compact clusters.

A system to provide online navigation recommendation for Web visitors is introduced. We call visitor the anonymous user, i.e., when only data about her/his browsing behavior (Web logs) are available. We first apply clustering techniques over a large sample of Web data. Next, from the significant patterns that are discovered, a set of rules about how to use them is created. Finally, comparing the current Web visitor session with the patterns, online navigation recommendations are proposed using the mentioned rules. The system was tested using data from a real Web site, showing its effectiveness.

We proposed an idea to achieve a sense of eye contact in multiparty videoconference (VC) by applying an image placement paradigm for four positions of a camera on the display. The concept is to find the relation between face's turning direction and image's position that can serve a sense of eye contact. The experimental results proved the merits of proposed idea to achieve the sense of reality in multiparty videoconference.

Web sites have become necessary channels for effective marketing and efficient operation for almost every company. To maintain and update a site is, however, a non-trivial task due to the complexity of the underlying business and the dynamic visitor behavior. Tools for the reconfiguration of web sites offer help for such tasks. We propose a methodology for the reconfiguration of web structure and content. Its application to the web site of a Chilean bank shows its benefits.

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A Web site is a semi structured collection of different kinds of data, whose motivation is to show relevant information to a visitor and in this way capture her/his attention. Understanding the specific preferences that define the visitor behavior in a Web site is a complex task. An approximation is supposed that depends on the content, navigation sequence and time spent in each page visited. These variables can be extracted from the Web log files and the Web site itself, using Web usage and content mining respectively. Combining the described variables, a similarity measure among visitor sessions is introduced and used in a clustering algorithm, which identifies groups of similar sessions, allowing the analysis of visitor behavior. In order to prove the methodology's effectiveness, it was applied in a certain Web site, showing the benefits of the described approach.

The continuous improvement of a Web site's content, can be the key to attract new customers or maintain the existing ones. A way to obtain such improvement, is to study the behavior of a user while browsing in the Web. For the analysis of this behavior two variables are of particular interest: the pages visited during a user session and the time spent in each one of them. The respective Web log files contain part of this data. These files, however, can contain a huge number of registers where large part of them possibly do not contain relevant information. This is one of the reasons why finding initially unknown and useful relations in Web log registers is a complex task, which can be performed applying the process of knowledge discovery in databases (KDD). We propose a methodology for Web mining based on a data mart model. We applied this methodology analyzing log files from a certain Web site. The respective results, gave very important insights regarding visitors behavior and preferences. This knowledge has been used in the Web site's reconfiguration.

Network support is essential to provide a quality multimedia presentation for a large-scale video archival and retrieval system. This paper proposes an efficient video delivery method based on the concept of active networks, for the multiple thumbnail video digest display system that we have been developing to solve two problems: (i) half of the users stopped their requests near the beginning of the video playback; and (ii) a large amount of time was consumed for retrieving a video. In the simulations conducted at our research lab, the video server load can be reduced by around 40%, and the total waiting time for receiving all the grouped videos that clients may experience can also be reduced significantly.

A novel head-tracking technique for a full-color stereoscopic video display system is presented. Using this head-tracking system, the stereoscopic area is increased by a factor of three and stabilized stereoscopic images are observed. Two kinds of large-size rear-projection stereoscopic display systems that do not require special glasses are proposed. Experimental tests, confirming the effectiveness of the proposed systems, are described. By incorporating these techniques into visual communication systems, such as TV telephone systems, video conference systems, or virtual reality systems, a very natural and accurate depth sensation which cannot be obtained by conventional systems that require the viewer to wear special glasses may be possible.>

Statistical characteristics of video signals for video packet coding, are clarified and a variable-bit-rate coding method for asynchronous transfer mode (ATM) networks is described that is capable of compensating for packet loss. ATM capabilities are shown to be greatly affected by delay, delay jitter, and packet loss probability. Packet loss has the greatest influence on picture quality. Packets may be lost either due to random bit error in a cell header or to network control when traffic is congested. A layered coding technique using discrete-cosine transform (DCT) coding is presented which is suitable for packet loss compensation. The influence of packet loss on picture quality is discussed, and decoded pictures with packet loss are shown. The proposed algorithm was verified by computer simulations.>

An interframe coding system is described which is capable of transmitting both 4-MHz videotelephone signals and NTSC color TV signals at an average rate of 6.312 Mbits/s. The coding system takes as much advantage as possible of in-frame correlation, in addition to interframe correlation, by coding pel-to-pel difference of interframe difference (combinational difference). Variable-length word coding is effectively used to reduce the number of bits to be transmitted. In order to prevent buffer overflow, luminance and spatial resolutions are controlled. To control luminance resolution, interframe difference is multiplied by a coefficient\alpha \leq 1, so that value approaches zero when violent motion causes the buffer to fill. Though multiplication with small α results in temporal resolution reduction in moving areas, quality degradation is not too marked. Spatial resolution control is achieved by using subsample and subline coding modes.

As a powerful tool for economizing on the digital transmission of videotelephone signals, interframe coding techniques have drawn increasing attention, and various interframe coding schemes have been proposed recently. However, many problems remain to be studied in establishing technical feasibility for these techniques and schemes. This concise paper reports on field trial in which a 1.544-Mbits/s digital transmission of videotelephone signals was conducted over Nippon Telegraph and Telephone Public Corporation's (NTT's) PCM100M digital repeatered line between Tokyo and Yokohama, Japan. The experimental interframe codec is based on the well-understood conditional picture element replenishment algorithm. The object of the field trial was to demonstrate, not the validity of the algorithm itself, but the technical feasibility of the interframe coding system as an entity. Experimental results are satisfactory enough to assure us of the system's practicality in the near future.