81 research outputs found
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Estimation of trailer off-tracking using visual odometry
High Capacity Vehicles (HCVs) have been shown to be highly effective in reducing emissions associated with road freight transport. However, the reduced manoeuvrability of long vehicles often necessitates the use of active trailer steering. Path-following trailer steering systems are very effective in this regard, but are currently limited to on-highway applications due to the manner in which trailer off-tracking is estimated. In this work, a novel trailer off- tracking measurement concept is introduced which is independent of wheel slip and ground surface conditions, and requires no additional sensor measurements or parameter data from the tractor. The concept utilises a stereo camera pair affixed to the trailer and a visual odometry-based algorithm to calculate off-tracking. The concept was evaluated in detailed simulation and full-scale vehicle tests, demonstrating its feasibility and highlighting some important characteristics. RMS measurement errors of 0.11-0.12 m (3.3-3.6%) were obtained in a challenging visual environment.CSIR, South Africa;
Cambridge Commonwealth, European and International Trust, UK;
Cambridge Vehicle Dynamics Consortium
If deep learning is the answer, then what is the question?
Neuroscience research is undergoing a minor revolution. Recent advances in
machine learning and artificial intelligence (AI) research have opened up new
ways of thinking about neural computation. Many researchers are excited by the
possibility that deep neural networks may offer theories of perception,
cognition and action for biological brains. This perspective has the potential
to radically reshape our approach to understanding neural systems, because the
computations performed by deep networks are learned from experience, not
endowed by the researcher. If so, how can neuroscientists use deep networks to
model and understand biological brains? What is the outlook for neuroscientists
who seek to characterise computations or neural codes, or who wish to
understand perception, attention, memory, and executive functions? In this
Perspective, our goal is to offer a roadmap for systems neuroscience research
in the age of deep learning. We discuss the conceptual and methodological
challenges of comparing behaviour, learning dynamics, and neural representation
in artificial and biological systems. We highlight new research questions that
have emerged for neuroscience as a direct consequence of recent advances in
machine learning.Comment: 4 Figures, 17 Page
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Vision-based trailer pose estimation for articulated vehicles
Articulated Heavy Goods Vehicles (HGVs) are more efficient than conventional rigid lorries, but exhibit reduced low-speed manoeuvrability and high-speed stability. Technologies such as autonomous reversing and path-following trailer steering can mitigate this, but practical limitations of the available sensing technologies restrict their commercialisation potential. This dissertation describes the development of practical vision-based articulation angle and trailer off-tracking sensing for HGVs.
Chapter 1 provides a background and literature review, covering important vehicle technologies, existing commercial and experimental sensors for articulation angle and off-tracking measurement, and relevant vision-based technologies. This is followed by an introduction to pertinent computer vision theory and terminology in Chapter 2.
Chapter 3 describes the development and simulation-based assessment of an articulation angle sensing concept. It utilises a rear-facing camera mounted behind the truck or tractor, and one of two proposed image processing methods: template-matching and Parallel Tracking and Mapping (PTAM). The PTAM-based method was shown to be the more accurate and versatile method in full-scale vehicle tests. RMS measurement errors of 0.4-1.6 were observed in tests on a tractor semi-trailer (Chapter 4), and 0.8-2.4 in tests on a Nordic combination with two articulation points (Chapter 5). The system requires no truck-trailer communication links or artificial markers, and is compatible with multiple trailer shapes, but was found to have increasing errors at higher articulation angles.
Chapter 6 describes the development and simulation-based assessment of a trailer off-tracking sensing concept, which utilises a trailer-mounted stereo camera pair and visual odometry. The concept was evaluated in full-scale tests on a tractor semi-trailer combination in which camera location and stereo baseline were varied, presented in Chapter 7. RMS measurement errors of 0.11-0.13 m were obtained in some tests, but a sensitivity to camera alignment was discovered in others which negatively affected results. A very stiff stereo camera mount with a sub-0.5 m baseline is suggested for future experiments.
A summary of the main conclusions, a review of the objectives, and recommendations for future work are given in Chapter 8. Recommendations include further refinement of both sensors, an investigation into lighting sensitivity, and alternative applications of the sensors.This work was supported by a "CSIR South Africa Cambridge Scholarship", funded jointly by the Cambridge Commonwealth, European & International Trust and the Council for Scientific & Industrial Research (CSIR South Africa)
Performance-based standards for South African car-carriers
Until recently, car-carriers in South Africa operated under abnormal load permits
allowing a finite relaxation of legal height and length limits. This practice is being phased
out, and exemption will only be granted if a car-carrier complies with the Australian
Performance-Based Standards (PBS) scheme. A low-speed turning model was developed
in Matlab®, and used to benchmark the tail swing performance of the existing South
African car-carrier fleet. About 80 per cent of the fleet were shown to not comply with
the 0.30 m tail swing limit, due to South Africa’s inadequate rear overhang legislation
which permits tail swing of up to 1.25 m. TruckSim® was used to conduct detailed PBS
assessments of two car-carrier designs. Critical performance areas were identified; most
notably yaw damping and tail swing for the truck and tag-trailer combination, and
maximum of difference and difference of maxima for the tractor and semitrailer
combination. These were remedied through appropriate design modifications. The
Matlab® model was shown to be versatile, accurate and efficient, with potential for future
application. The TruckSim® assessments highlighted complexities unique to car-carriers
in a PBS context and showed how these may be addressed. This research has shown the
benefit of PBS for heavy vehicles, and has guided car-carrier design to improve safety
Neural knowledge assembly in humans and neural networks
Human understanding of the world can change rapidly when new information comes to light, such as when a plot twist occurs in a work of fiction. This flexible "knowledge assembly" requires few-shot reorganization of neural codes for relations among objects and events. However, existing computational theories are largely silent about how this could occur. Here, participants learned a transitive ordering among novel objects within two distinct contexts before exposure to new knowledge that revealed how they were linked. Blood-oxygen-level-dependent (BOLD) signals in dorsal frontoparietal cortical areas revealed that objects were rapidly and dramatically rearranged on the neural manifold after minimal exposure to linking information. We then adapt online stochastic gradient descent to permit similar rapid knowledge assembly in a neural network model
Orthogonal representations for robust context-dependent task performance in brains and neural networks
How do neural populations code for multiple, potentially conflicting tasks? Here we used computational simulations involving neural networks to define “lazy” and “rich” coding solutions to this context-dependent decision-making problem, which trade off learning speed for robustness. During lazy learning the input dimensionality is expanded by random projections to the network hidden layer, whereas in rich learning hidden units acquire structured representations that privilege relevant over irrelevant features. For context-dependent decision-making, one rich solution is to project task representations onto low-dimensional and orthogonal manifolds. Using behavioral testing and neuroimaging in humans and analysis of neural signals from macaque prefrontal cortex, we report evidence for neural coding patterns in biological brains whose dimensionality and neural geometry are consistent with the rich learning regime
Are task representations gated in macaque prefrontal cortex?
A recent paper (Flesch et al, 2022) describes behavioural and neural data
suggesting that task representations are gated in the prefrontal cortex in both
humans and macaques. This short note proposes an alternative explanation for
the reported results from the macaque data
Abrupt and spontaneous strategy switches emerge in simple regularised neural networks
Humans sometimes have an insight that leads to a sudden and drastic
performance improvement on the task they are working on. Sudden strategy
adaptations are often linked to insights, considered to be a unique aspect of
human cognition tied to complex processes such as creativity or meta-cognitive
reasoning. Here, we take a learning perspective and ask whether insight-like
behaviour can occur in simple artificial neural networks, even when the models
only learn to form input-output associations through gradual gradient descent.
We compared learning dynamics in humans and regularised neural networks in a
perceptual decision task that included a hidden regularity to solve the task
more efficiently. Our results show that only some humans discover this
regularity, whose behaviour was marked by a sudden and abrupt strategy switch
that reflects an aha-moment. Notably, we find that simple neural networks with
a gradual learning rule and a constant learning rate closely mimicked
behavioural characteristics of human insight-like switches, exhibiting delay of
insight, suddenness and selective occurrence in only some networks. Analyses of
network architectures and learning dynamics revealed that insight-like
behaviour crucially depended on a regularised gating mechanism and noise added
to gradient updates, which allowed the networks to accumulate "silent
knowledge" that is initially suppressed by regularised (attentional) gating.
This suggests that insight-like behaviour can arise naturally from gradual
learning in simple neural networks, where it reflects the combined influences
of noise, gating and regularisation.Comment: 17 pages, 5 figure
Reproducibility in high-throughput density functional theory: a comparison of AFLOW, Materials Project, and OQMD
A central challenge in high throughput density functional theory (HT-DFT)
calculations is selecting a combination of input parameters and post-processing
techniques that can be used across all materials classes, while also managing
accuracy-cost tradeoffs. To investigate the effects of these parameter choices,
we consolidate three large HT-DFT databases: Automatic-FLOW (AFLOW), the
Materials Project (MP), and the Open Quantum Materials Database (OQMD), and
compare reported properties across each pair of databases for materials
calculated using the same initial crystal structure. We find that HT-DFT
formation energies and volumes are generally more reproducible than band gaps
and total magnetizations; for instance, a notable fraction of records disagree
on whether a material is metallic (up to 7%) or magnetic (up to 15%). The
variance between calculated properties is as high as 0.105 eV/atom (median
relative absolute difference, or MRAD, of 6%) for formation energy, 0.65
{\AA}/atom (MRAD of 4%) for volume, 0.21 eV (MRAD of 9%) for band gap, and
0.15 /formula unit (MRAD of 8%) for total magnetization,
comparable to the differences between DFT and experiment. We trace some of the
larger discrepancies to choices involving pseudopotentials, the DFT+U
formalism, and elemental reference states, and argue that further
standardization of HT-DFT would be beneficial to reproducibility.Comment: Authors VIH and CKHB contributed equally to this wor
Simple inhibitors of histone deacetylase activity that combine features of short-chain fatty acid and hydroxamic acid inhibitors
Butyric acid and trichostatin A (TSA) are anti-cancer compounds that cause the upregulation of genes involved in differentiation and cell cycle regulation by inhibiting histone deacetylase (HDAC) activity. In this study we have synthesized and evaluated compounds that combine the bioavailability of short-chain fatty acids, like butyric acid, with the bidentate binding ability of TSA. A series of analogs were made to examine the effects of chain length, simple aromatic cap groups, and substituted hydroxamates on the compounds\u27 ability to inhibit rat-liver HDAC using a fluorometric assay. In keeping with previous structure-activity relationships, the most effective inhibitors consisted of longer chains and hydroxamic acid groups. It was found that 5-phenylvaleric hydroxamic acid and 4-benzoylbutyric hydroxamic acid were the most potent inhibitors with IC50\u27s of 5 microM and 133 microM respectively
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