Approximate reinforcement learning to control beaconing congestion in distributed networks

In vehicular communications, the increase of the channel load caused by excessive periodical messages (beacons) is an important aspect which must be controlled to ensure the appropriate operation of safety applications and driver-assistance systems. To date, the majority of congestion control solutions involve including additional information in the payload of the messages transmitted, which may jeopardize the appropriate operation of these control solutions when channel conditions are unfavorable, provoking packet losses. This study exploits the advantages of non-cooperative, distributed beaconing allocation, in which vehicles operate independently without requiring any costly road infrastructure. In particular, we formulate the beaconing rate control problem as a Markov Decision Process and solve it using approximate reinforcement learning to carry out optimal actions. Results obtained were compared with other traditional solutions, revealing that our approach, called SSFA, is able to keep a certain fraction of the channel capacity available, which guarantees the delivery of emergency-related notifications with faster convergence than other proposals. Moreover, good performance was obtained in terms of packet delivery and collision ratios.

Imaging lidar prototype with homography and deep learning ranging methods

We report on developing a non-scanning laser-based imaging lidar system based on a diffractive optical element with potential applications in advanced driver assistance systems, autonomous vehicles, drone navigation, and mobile devices. Our proposed lidar utilizes image processing, homography, and deep learning. Our emphasis in the design approach is on the compactness and cost of the final system for it to be deployable both as standalone and complementary to existing lidar sensors, enabling fusion sensing in the applications. This work describes the basic elements of the proposed lidar system and presents two potential ranging mechanisms, along with their experimental results demonstrating the real-time performance of our first prototype.

A deep learning-based worker assistance system for error prevention: Case study in a real-world manual assembly

Modern assembly systems adapt to the requirements of customised and short-lived products. As assembly tasks become increasingly complex and change rapidly, the cognitive load on employees increases. This leads to the use of assistance systems for manual assembly to detect and avoid human errors and thus ensure consistent product quality. Most of these systems promise to improve the production environment but have hardly been studied quantitatively so far. Recent advances in deep learning-based computer vision have also not yet been fully exploited. This study aims to provide architectural, and implementational details of a state-of-the-art assembly assistance system based on an object detection model. The proposed architecture is intended to be representative of modern assistance systems. The error prevention potential is determined in a case study in which test subjects manually assemble a complex explosion-proof tubular lamp. The results show 51 % fewer assembly errors compared to a control group without assistance. Three of the four considered types of error classes have been reduced by at least 42 %. In particular, errors by omission are most likely to be prevented by the system. The reduction in the error rate is observed over the entire period of 30 consecutive product assemblies, comparing assisted and unassisted assembly. Furthermore, the recorded assembly data are found to be valuable regarding traceability and production improvement processes.

User Evaluation of Passenger Assistance System Concepts on Public Highways

There is ample research on assistance systems for drivers in conventional and automated vehicles. In the past, those systems were developed to increase safety but also to increase driver comfort. Since many common risks have by now been mitigated through such systems, the research and development focus expanded to also include comfort-related assistance. However, the passenger has rarely been taken into account explicitly, although it has been shown that passenger discomfort is a relevant problem. Therefore, this work investigated the potential of passenger assistance systems to reduce such discomfort. Three different passenger assistant system prototypes were tested in a driving study on public highway with N = 19 participants. The systems provided information about parameters related to the performance of the driver and one additionally provided a communicative means of influence. For two passenger assistant systems, it could be shown that they significantly reduced passenger discomfort in at least a subset of the evaluated situations. The majority of participants rated one or multiple of the assistant systems as more comfortable than a ride without assistance. The system providing information about the attentiveness of the driver was most effective in reducing discomfort and was rated as the most helpful system. The results show that explicitly considering the situation of passengers in the design of assistance systems can positively impact their comfort. This can be achieved using information from common systems targeting driver assistance available to the passenger.

Cognitive compatibility in modern manual mixed-model assembly systems

The compatibility concept is widely used in psychology and ergonomics. It describes the fit between elements of a sociotechnical system which is a prerequisite to successfully cooperate towards a common goal. For at least three decades, cognitive compatibility is of increasing importance. It describes the fit of externally presented information, information processing, and the required motor action. However, with increasing system complexity, probability for incompatibility increases, too, leading to time losses, errors and overall degraded performance. The elimination of cognitive incompatibilities through ergonomic measures at the workplace requires a lot of creativity and effort. Using practical examples from mixed-model assembly, improved information management and the use of informational assistance systems are discussed as promising ergonomic approaches. The ultimate goal is to avoid cognitive overload, for example in part picking or assembly tools choosing. To find a fit between externally mediated work instructions via displays and the subjectively used internal models and competencies is a challenging task. Only if this fit is given the system is perceived as beneficial. To achieve this, the assistance system should be configurable to fit individual needs as far as possible. Successful system design requires early participation and comprehensive integration of the assistance systems into the existing IT infrastructure.Practical relevance: Varied manual assembly requires a high degree of cognitive work. A rise in complexity of the assembly task increases the risk that cognitive incompatibility and thus cognitive overload will occur more frequently. It is shown that such unhealthy conditions can be countered by better information presentation and by the use of individually adaptable informational assistance systems.

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

Driver inattention is the primary cause of vehicle accidents; hence, manufacturers have introduced systems to support the driver and improve safety; nonetheless, advanced driver assistance systems (ADAS) must be properly designed not to become a potential source of distraction for the driver due to the provided feedback. In the present study, an experiment involving auditory and haptic ADAS has been conducted involving 11 participants, whose attention has been monitored during their driving experience. An RGB-D camera has been used to acquire the drivers’ face data. Subsequently, these images have been analyzed using a deep learning-based approach, i.e., a convolutional neural network (CNN) specifically trained to perform facial expression recognition (FER). Analyses to assess possible relationships between these results and both ADAS activations and event occurrences, i.e., accidents, have been carried out. A correlation between attention and accidents emerged, whilst facial expressions and ADAS activations resulted to be not correlated, thus no evidence that the designed ADAS are a possible source of distraction has been found. In addition to the experimental results, the proposed approach has proved to be an effective tool to monitor the driver through the usage of non-invasive techniques.

Behind-The-Scenes (BTS): Wiper-Occlusion Canceling for Advanced Driver Assistance Systems in Adverse Rain Environments

Driving in an adverse rain environment is a crucial challenge for vision-based advanced driver assistance systems (ADAS) in the automotive industry. The vehicle windshield wiper removes adherent raindrops that cause distorted images from in-vehicle frontal view cameras, but, additionally, it causes an occlusion that can hinder visibility at the same time. The wiper-occlusion causes erroneous judgments by vision-based applications and endangers safety. This study proposes behind-the-scenes (BTS) that detects and removes wiper-occlusion in real-time image inputs under rainy weather conditions. The pixel-wise wiper masks are detected by high-pass filtering to predict the optical flow of a sequential image pair. We fine-tuned a deep learning-based optical flow model with a synthesized dataset, which was generated with pseudo-ground truth wiper masks and flows using auto-labeling with acquired real rainy images. A typical optical flow dataset with static synthetic objects is synthesized with real fast-moving objects to enhance data diversity. We annotated wiper masks and scenes as detection ground truths from the collected real images for evaluation. BTS outperforms by achieving a 0.962 SSIM and 91.6% F1 score in wiper mask detection and 88.3% F1 score in wiper image detection. Consequently, BTS enhanced the performance of vision-based image restoration and object detection applications by canceling occlusions and demonstrated it potential role in improving ADAS under rainy weather conditions.