Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

Personalised driving status recognition and monitoring

A number of advanced driver assistance systems have recently been developed and implemented to improve road safety and driving experience. However, most of them are designed based on generic driver behaviours, where driving characteristics and preferences of a specific driver are neglected. Therefore, it is not surprising that conventional driving status recognition systems may lead to degraded performance for a particular driver. To this end, driving status recognition systems should be personalised to enhance system performance. This thesis aims to develop driving status recognition systems of high accuracy in order to ensure driving safety. [Continues.

Domain-adapted driving scene understanding with uncertainty-aware and diversified GANs

Autonomous vehicles are required to operate in an uncertain environment. Recent advances in computational intelligence (CI) techniques make it possible to understand driving scenes in various environments by using a semantic segmentation neural network, which assigns a class label to each pixel. It requires massive pixel-level labelled data to optimise the network. However, it is challenging to collect sufficient data and labels in the real world. An alternative solution is to obtain synthetic dense pixel-level labelled data from a driving simulator. Although the use of synthetic data is a promising way to alleviate the labelling problem, models trained with virtual data cannot generalise well to realistic data due to the domain shift. To fill this gap, we propose a novel uncertainty-aware generative ensemble method. In particular, ensembles are obtained from different optimisation objectives, training iterations, and network initialisation so that they are complementary to each other to produce reliable predictions. Moreover, an uncertainty-aware ensemble scheme is developed to derive fused prediction by considering the uncertainty from ensembles. Such a design can make better use of the strengths of ensembles to enhance adapted segmentation performance. Experimental results demonstrate the effectiveness of our method on three large-scale datasets.</p