Dynamic Conditional Imitation Learning for Autonomous Driving

Conditional imitation learning (CIL) trains deep neural networks, in an end-to-end manner, to mimic human driving. This approach has demonstrated suitable vehicle control when following roads, avoiding obstacles, or taking specific turns at intersections to reach a destination. Unfortunately, performance dramatically decreases when deployed to unseen environments and is inconsistent against varying weather conditions. Most importantly, the current CIL fails to avoid static road blockages. In this work, we propose a solution to those deficiencies. First, we fuse the laser scanner with the regular camera streams, at the features level, to overcome the generalization and consistency challenges. Second, we introduce a new efficient Occupancy Grid Mapping (OGM) method along with new algorithms for road blockages avoidance and global route planning. Consequently, our proposed method dynamically detects partial and full road blockages, and guides the controlled vehicle to another route to reach the destination. Following the original CIL work, we demonstrated the effectiveness of our proposal on CARLA simulator urban driving benchmark. Our experiments showed that our model improved consistency against weather conditions by four times and autonomous driving success rate generalization by 52%. Furthermore, our global route planner improved the driving success rate by 37%. Our proposed road blockages avoidance algorithm improved the driving success rate by 27%. Finally, the average kilometers traveled before a collision with a static object increased by 1.5 times. The main source code can be reached at https://heshameraqi.github.io/dynamic_cil_autonomous_driving.Comment: 14 pages, 11 figures, 7 table

Understanding student experience in the age of personalised study

Moves in higher education to provide personalised learning for students increase the importance of gaining and maintaining an understanding of the student experience. For some institutions, this increase in complexity may stretch current systems and data structures. The complexity is amplified where multiple start dates are offered to improve the personalisation of study. The Open University, OU, has over the years, continued to develop its Supported Open Learning, SOL, methods and as an institution is now prioritising Personalised Open Learning, POL. This increases the importance of accessible detailed pathway information. We describe the development of one possible approach intended to provide greater understanding of the student experience for staff interpreting progress data. Another outcome of personalisation is the fragmentation of student cohorts, as individuals each make their own study choices while progressing towards their study goal. A relatively straightforward programme of study can lead to 64 different study routes creating a further challenge for staff in understanding the differing student experiences. We show how this can be represented in a simple data structure that allows powerful queries. Our approach uses a multi-model database, with graphical capabilities. By creating this structure in the ArangoDB environment it was possible to readily test it with 150,000 records and query it using graphical queries in the native AQL language. The early response from faculty colleagues is very positive. They appreciate the graphical output and the ability to straightforwardly answer their questions on whether students experience greater success on one study route rather than another. We are therefore continuing to develop this model to support a qualification review for summer 2018. In our presentation we will describe the challenge and illustrate an approach we are taking: giving examples of the queries we are using and the kinds of data the system outputs

The topological reading of ambiances in the built environment: the new methodology for the analysis of the luminous ambiance in the museum space.

Daylight is currently at the centre of discourse on architectural space. The definition of architectural space takes essence from Euclidean geometry related to metric dimensions. The present study is an attempt to shed light on topology which is a non-Euclidean geometry. It can support non-metric components of space such as light to define architectural space. A corpus of six European museums has been chosen to study the immaterial or material relationships between form and daylight, since light is an essential element for the success of the exhibition. It also seeks to highlight discontinuity reports, and to confirm their existence through their software visualizations. Therefore, the current study has taken into account an analysis model based on the notions of "route" and "sequence". The contemporary architectural project focused on taking into account human postures, both physical and psychological, within the architectural space. The results obtained show that light can release other spatial features for the museum space that can be highlighted by visualization with sequential analysis

Expanding Higher Education in the UK: From 'System Slowdown' to 'System Acceleration'

This paper sets out to explore the implications of current patterns of participation and attainment, particularly among 16-19 year olds, for the further expansion of higher education in the UK. It uses a range of recent statistics on participation and attainment to describe what is termed ‘system slowdown’. It then goes on to explore a basis for ‘system acceleration’ through the development of five possible routes into higher education both for 16-19 year olds and for adults. We conclude the paper by looking briefly at a number of inter-related strategies the Government could adopt to encourage ‘system acceleration’. We suggest that unless the Government is prepared to consider policy changes of this type, it is unlikely to reach the higher education participation target it has set itself and may also jeopardise the basis for a sustainable lifelong learning system for the 21st century

Identifying Factors Contributing to Differences in Success Rates Among Three Montana TRIO Upward Bound Programs

TRIO is a federally funded program put into place to assist students from disadvantaged backgrounds reach postsecondary education. Southwest Montana TRIO assists Helena High School, Capital High School, Anaconda High School, and Butte High School. Helena, Anaconda, and Butte share similar demographics. Because of this, one might expect that the academic success rates would be fairly similar. My research indicates there are in fact significant differences. In order to best assist the schools assisted by Southwest Montana TRIO it is important to understand why differences are taking place. By using the U.S Department of Education standards to back this up and using and using mixed methods of quantitative and qualitative research the differences were more easily identified. These differences could be attributed to measurable aspects such as financial stability, families academic history, and the lack of a consistent program coordinator. Differences could also be attributed to nonmeasurable aspects such as personal struggles, lack of motivation, or if a student is only incentive driven meaning they are only involved with the program for the many perks that come along with it. My research indicates that all of these factors partly contribute to a student’s success in achieving TRIO’s goals, or failing to do so. Each student faces struggles often only known to them. Knowing this, it is up to the people of TRIO to adapt and find the next route of action to take with each student in order to help them find their success. It is up to those people to make the difference and having a great understanding that struggles happen

Vocational ladders or crazy paving? Making your way to higher levels

The report is part of a suite of research projects on apprenticeships under the overall theme of 'making work-based learning work'. The aim of this particular study was to explore the role of level 3 vocational qualifications and work-based learning, including Modern Apprenticeships, as progression routes to higher education and to higher-level knowledge and skills more generally. The study comprised secondary analysis of national datasets, and an exploration of supply, demand and progression patterns in four contrasting employment sectors, focusing on enablers and inhibitors of work-based education and training

Improving retention for all students, studying mathematics as part of their chosen qualification, by using a voluntary diagnostic quiz

This case study demonstrates the issues and advantages in encouraging students to take responsibility for their learning and to be better prepared both in terms of knowledge and expectations for their study. The study outlines the improvement in retention achieved when students were encouraged to use a voluntary diagnostic quiz on a first year university mathematics module. Initially the power of the diagnostic quiz, in predicting future success on the module, was identified using predictive analytics. Students were contacted by experienced Education Guidance staff who encouraged them to take the quiz prior to course start with the aim of using their results to steer them to start on the “right” course. The diagnostic quiz total score was made available to the student’s course tutor prior to course start to enable further tailoring of support to individual students. Early indications show an improvement in early module retention. The module in this case study was for distance learning students on an open access mathematics course