Efficient resource allocation for automotive active vision systems

Individual mobility on roads has a noticeable impact upon peoples' lives, including traffic accidents resulting in severe, or even lethal injuries. Therefore the main goal when operating a vehicle is to safely participate in road-traffic while minimising the adverse effects on our environment. This goal is pursued by road safety measures ranging from safety-oriented road design to driver assistance systems. The latter require exteroceptive sensors to acquire information about the vehicle's current environment. In this thesis an efficient resource allocation for automotive vision systems is proposed. The notion of allocating resources implies the presence of processes that observe the whole environment and that are able to effeciently direct attentive processes. Directing attention constitutes a decision making process dependent upon the environment it operates in, the goal it pursues, and the sensor resources and computational resources it allocates. The sensor resources considered in this thesis are a subset of the multi-modal sensor system on a test vehicle provided by Audi AG, which is also used to evaluate our proposed resource allocation system. This thesis presents an original contribution in three respects. First, a system architecture designed to efficiently allocate both high-resolution sensor resources and computational expensive processes based upon low-resolution sensor data is proposed. Second, a novel method to estimate 3-D range motion, e cient scan-patterns for spin image based classifiers, and an evaluation of track-to-track fusion algorithms present contributions in the field of data processing methods. Third, a Pareto efficient multi-objective resource allocation method is formalised, implemented, and evaluated using road traffic test sequences

Multimodal information presentation for high-load human computer interaction

This dissertation addresses the question: given an application and an interaction context, how can interfaces present information to users in a way that improves the quality of interaction (e.g. a better user performance, a lower cognitive demand and a greater user satisfaction)? Information presentation is critical to the quality of interaction because it guides, constrains and even determines cognitive behavior. A good presentation is particularly desired in high-load human computer interactions, such as when users are under time pressure, stress, or are multi-tasking. Under a high mental workload, users may not have the spared cognitive capacity to cope with the unnecessary workload induced by a bad presentation. In this dissertation work, the major presentation factor of interest is modality. We have conducted theoretical studies in the cognitive psychology domain, in order to understand the role of presentation modality in different stages of human information processing. Based on the theoretical guidance, we have conducted a series of user studies investigating the effect of information presentation (modality and other factors) in several high-load task settings. The two task domains are crisis management and driving. Using crisis scenario, we investigated how to presentation information to facilitate time-limited visual search and time-limited decision making. In the driving domain, we investigated how to present highly-urgent danger warnings and how to present informative cues that help drivers manage their attention between multiple tasks. The outcomes of this dissertation work have useful implications to the design of cognitively-compatible user interfaces, and are not limited to high-load applications

Situation-Aware Environment Perception Using a Multi-Layer Attention Map

Within the field of automated driving, a clear trend in environment perception tends towards more sensors, higher redundancy, and overall increase in computational power. This is mainly driven by the paradigm to perceive the entire environment as best as possible at all times. However, due to the ongoing rise in functional complexity, compromises have to be considered to ensure real-time capabilities of the perception system. In this work, we introduce a concept for situation-aware environment perception to control the resource allocation towards processing relevant areas within the data as well as towards employing only a subset of functional modules for environment perception, if sufficient for the current driving task. Specifically, we propose to evaluate the context of an automated vehicle to derive a multi-layer attention map (MLAM) that defines relevant areas. Using this MLAM, the optimum of active functional modules is dynamically configured and intra-module processing of only relevant data is enforced. We outline the feasibility of application of our concept using real-world data in a straight-forward implementation for our system at hand. While retaining overall functionality, we achieve a reduction of accumulated processing time of 59%.Comment: Update 1: Removed minor typos, corrected FKZ. Update 2: Extension of Section III-E. Rewording in some sections to improve clarity. Added IEEE Copyright Notic

A proposed psychological model of driving automation

This paper considers psychological variables pertinent to driver automation. It is anticipated that driving with automated systems is likely to have a major impact on the drivers and a multiplicity of factors needs to be taken into account. A systems analysis of the driver, vehicle and automation served as the basis for eliciting psychological factors. The main variables to be considered were: feed-back, locus of control, mental workload, driver stress, situational awareness and mental representations. It is expected that anticipating the effects on the driver brought about by vehicle automation could lead to improved design strategies. Based on research evidence in the literature, the psychological factors were assembled into a model for further investigation

Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific and important applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future work are discussed to further enhance the effectiveness and efficiency of FL in the context of CAV

Lidar waveform based analysis of depth images constructed using sparse single-photon data

This paper presents a new Bayesian model and algorithm used for depth and intensity profiling using full waveforms from the time-correlated single photon counting (TCSPC) measurement in the limit of very low photon counts. The model proposed represents each Lidar waveform as a combination of a known impulse response, weighted by the target intensity, and an unknown constant background, corrupted by Poisson noise. Prior knowledge about the problem is embedded in a hierarchical model that describes the dependence structure between the model parameters and their constraints. In particular, a gamma Markov random field (MRF) is used to model the joint distribution of the target intensity, and a second MRF is used to model the distribution of the target depth, which are both expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm is then proposed to compute the Bayesian estimates of interest and perform Bayesian inference. This algorithm is equipped with a stochastic optimization adaptation mechanism that automatically adjusts the parameters of the MRFs by maximum marginal likelihood estimation. Finally, the benefits of the proposed methodology are demonstrated through a serie of experiments using real data

The influence of innovation in tangible and intangible resource allocation: a qualitative multi case study

Considering the current turbulent macroeconomic environment, the aim of this research is to explore the influence of innovation in tangible and intangible resource allocation. The literature underlines that organizations are facing a revolution in their business processes. As such, there is a need to understand the value of knowledge resources and to identify ways to manage them. This paper explores the field of resource allocation, namely dynamic capabilities, and highlights the importance of monitoring intangible resources. This research has three specific contributions. The first contribution provides a comprehensive picture of what has occurred in the field of tangible and intangible resource allocation, such as intellectual capital and its importance towards organizational performance. Secondly, it offers evidence about the actual need for performance measurement tools that foster intangible resource monitoring. Organizations devote special attention to market demands which consequently lead managers to adapt their strategies in areas concerning resource allocation. Given this importance, this research, comprising major innovative organizations in Portugal from diverse activity sectors, provides new insights and stresses the importance of tools to follow the overall performance of resource allocation. Managers of innovative organizations recognize the very powerful features of the Balanced Scorecard (BSC) in monitoring and linking strategic resources of both tangible and intangible natures. Thirdly, this research, with a view to enrich the field of intangible natures, points out some aspects for future research areas, bearing in mind the relevance of this research area confirmed by managers of the major innovative organizations. Thus, it provides prominent information for both academia and innovative organizations.This paper is financed by National Funds of the FCT – Portuguese Foundation for Science and Technology within the project «UIDB/03182/2020

Social Network Sites And Innovation Capabilities In The Uae Hotel Industry. Reliability And Normality Test

The integration of information technology through social network sites in business operating and management has been recognized as one of the most resource for the development of innovation capabilities. It plays a significant role in knowledge sharing and transform which are the seeds of innovation development. The service sector is now the main domain where IT plays is extensively integrated in operation and management functions. However, the literature still lacks of clear understanding about the concept of SNSs and the innovation capabilities in hotel industry which affect the effectively use IT in their businesses. Therefore, this study aims to model SNSs and innovation capabilities. The reliability test through Cronbach alpha as well as normality tests were used

Multiple Target, Multiple Type Filtering in the RFS Framework

A Multiple Target, Multiple Type Filtering (MTMTF) algorithm is developed using Random Finite Set (RFS) theory. First, we extend the standard Probability Hypothesis Density (PHD) filter for multiple types of targets, each with distinct detection properties, to develop a multiple target, multiple type filtering, N-type PHD filter, where $N\geq2$, for handling confusions among target types. In this approach, we assume that there will be confusions between detections, i.e. clutter arises not just from background false positives, but also from target confusions. Then, under the assumptions of Gaussianity and linearity, we extend the Gaussian mixture (GM) implementation of the standard PHD filter for the proposed N-type PHD filter termed the N-type GM-PHD filter. Furthermore, we analyze the results from simulations to track sixteen targets of four different types using a four-type (quad) GM-PHD filter as a typical example and compare it with four independent GM-PHD filters using the Optimal Subpattern Assignment (OSPA) metric. This shows the improved performance of our strategy that accounts for target confusions by efficiently discriminating them