Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research

TCPS as a specialised education stream

Transport CPS is a major area of growth and investment in many countries, but there is a significant shortage of engineers with the correct skills to build safe, secure, and efficient TCPS. It is suggested that a much broader education is required in CPS than has traditionally been the case for degree and Masters’ level students. CPS developers will need to be knowledgeable about natural, applied, and social sciences. The curriculum for a TCPS engineer emerges as a vast range of knowledge requirements and it is suggested that ideally students will graduate with a T-shaped curriculum vitae that provides for a broad knowledge of many subjects, and a deep knowledge of one or two. Studies into the appropriate curriculum for CPS have been conducted since the early 2000s, but it is noted that many of these failed to give adequate weight to topics in the social sciences and to ethics. A framework for TCPS graduate and post-graduate education is provided: delivery of the curriculum must ensure students are taught in a transdisciplinary manner and develop an holistic approach to TCPS development and operation

Temporal modelling of long-term heavy metal concentrations in aquatic ecosystems

This paper examines a series of connected and isolated lakes in the UK as a model system with historic episodes of heavy metal contamination. A 9-year hydrometeorological dataset for the sites was identified to analyse the legacy of heavy metal concentrations within the selected lakes based on physico-chemical and hydrometeorological parameters and, a comparison of the complementary methods of multiple regression, time series analysis and artificial neural network (ANN). The results highlight the importance of the quality of historic datasets without which analyses such as those presented in this research paper cannot be undertaken. The results also indicate that the ANNs developed were more realistic than the other methodologies (regression and time series analysis) considered. The ANNs provided a higher correlation coefficient and a lower mean squared error when compared to the regression models. However, quality assurance and pre-processing of the data was challenging and was addressed by transforming the relevant dataset and interpolating the missing values. The selection and application of the most appropriate temporal modelling technique, which relies on the quality of available dataset, is crucial for the management of legacy contaminated sites to guide successful mitigation measures to avoid significant environmental and human health implications.</p