Traffic Regulator Detection Using GPS Trajectories

This paper explores the idea of enriching maps with features predicted from GPS trajectories. More specifically, it proposes a method of classifying street intersections according to traffic regulators (traffic light, yield/priority-sign and right-of-way rule). Intersections are regulated locations and the observable movement of vehicles is affected by the underlying traffic rules. Movement patterns such as stop events or start-and-stop sequences are commonly observed at those locations due to traffic regulations. In this work, we test the idea of detecting traffic regulators by learning them in a supervised way from features derived from GPS trajectories. We explore and assess different settings of the feature vector being used to train a classifier that categorizes the intersections based on traffic regulators; also, we test several experimental setups. The results show that a Random Forest classifier with oversampling and Bagging booster enabled can predict the intersection regulators with 90.4% accuracy. We discuss future research directions and recommend next steps for improving the results of this research. © 2020, The Author(s)

Trajectory analysis at intersections for traffic rule identification

In this paper, we focus on trajectories at intersections regulated by various regulation types such as traffic lights, priority/yield signs, and right-of-way rules. We test some methods to detect and recognize movement patterns from GPS trajectories, in terms of their geometrical and spatio-temporal components. In particular, we first find out the main paths that vehicles follow at such locations. We then investigate the way that vehicles follow these geometric paths (how do they move along them). For these scopes, machine learning methods are used and the performance of some known methods for trajectory similarity measurement (DTW, Hausdorff, and Fréchet distance) and clustering (Affinity propagation and Agglomerative clustering) are compared based on clustering accuracy. Afterward, the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed- and time-profiles of trajectories. We show that depending on the regulation type, different movement patterns are observed at intersections. This finding can be useful for intersection categorization according to traffic regulations. The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information (traffic signs, traffic lights, etc.)

Analysis of mutations in alpha-synuclein and the protective effect of heat shock proteins in a model of alpha-synuclein-induced toxicity

Genetic studies have revealed three mutations (A30P, A53T and E46K) in alpha-synuclein (alpha-Syn) that cause Parkinson's disease (PD) in a small number of pedigrees with autosomal dominant inheritance. For the purpose of this thesis an in vitro model has been developed by stably over-expressing wild type (wt), A30P or A53T mutant alpha-Syn in ND7 neuronal cells. Wt alpha-Syn can enhance cell death in response to ischaemia/reoxygenation or staurosporine treatment whilst protecting against serum removal and dopamine-induced cell death in this system. In contrast, both mutant forms of alpha-Syn enhance cell death. The above stresses were used to induce primarily apoptotic cell death, implicated in PD pathology. Hence, the PD-associated mutations convert alpha-Syn from a protein which could modulate cell death differently in different circumstances to forms which are deleterious in response to various stresses. Subsequently, the neuroprotective effect of various heat shock proteins (hsps) in the above system was studied, utilising a Herpes Simplex Virus-based gene delivery system. For the first time, it was demonstrated that in an in vitro mammalian model of alpha-Syn-induced toxicity over-expression of hsp27 protects, under all the stresses tested, both wt and mutant alpha-Syn expressing cells, as assessed by multiple apoptotic/necrotic death assays. Interestingly, A30P alpha-Syn expressing cells were markedly protected by caspase-8 and caspase-9 inhibition as well as by hsp27 over-expression. No synergy between hsp27 and the caspase inhibitors was observed. In addition, hsp70 conferred protection only to wt alpha-Syn expressing cells exposed to ischaemia whereas hsp56 had no protective role in this system. Hence, hsp27 was neuroprotective by interfering with the enhanced caspase-dependent cell death resulting from mutant A30P alpha-Syn over-expression. Finally, studies of the mitochondrial status in this system were performed to further explore the site of action of hsp27. Hsp27 reduced significantly the mitochondrial membrane potential loss in stressed A30P mutant alpha-Syn cells and this correlates well with their enhanced cell survival. These findings suggest that hsp27 has a novel neuroprotective role against mutant alpha-Syn toxicity and this is achieved by interfering with the caspase cascade and mechanisms modulating the mitochondrial membrane potential

Traffic Control Recognition with AN Attention Mechanism Using Speed-Profile and Satellite Imagery Data

Traffic regulators at intersections act as an essential factor that influences traffic flow and, subsequently, the route choices of commuters. A digital map that provides up-to-date traffic control information is beneficial not only for facilitating the commuters’ trips, but also for energy-saving and environmental protection. In this paper, instead of using expensive surveying methods, we propose an automatic way based on a Conditional Variational Autoencoder (CVAE) to recognize traffic regulators, i. e., arm rules at intersections, by leveraging the GPS data collected from vehicles and the satellite imagery retrieved from digital maps, i. e., Google Maps. We apply a Long Short-Term Memory to extract the motion dynamics over a GPS sequence traversed through the intersection. Simultaneously, we build a Convolutional Neural Network (CNN) to extract the grid-based local imagery information associated with each step of the GPS positions. Moreover, a self-attention mechanism is adopted to extract the spatial and temporal features over both the GPS and grid sequences. The extracted temporal and spatial features are then combined for detecting the traffic arm rules. To analyze the performance of our method, we tested it on a GPS dataset collected by driving vehicles in Hannover, a medium-sized German city. Compared to a Random Forest model and an Encoder-Decoder model, our proposed model achieved better results with both accuracy and F1-score of 0.90 for the three-class (arm rules of uncontrolled, traffic light, and priority sign) task. We also carried out ablation studies to further investigate the effectiveness of the GPS input branch, the image input branch, and the self-attention mechanism in our model

Wrapping the alpha-crystallin domain fold in a chaperone assembly

Small heat shock proteins (sHsps) are oligomers that perform a protective function by binding denatured proteins. Although ubiquitous, they are of variable sequence except for a C-terminal similar to 90-residue "alpha-crystallin domain". Unlike larger stress response chaperones, sHsps are ATP-independent and generally form polydisperse assemblies. One proposed mechanism of action involves these assemblies breaking into smaller subunits in response to stress, before binding unfolding substrate and reforming into larger complexes. Two previously solved non-metazoan sHsp multimers are built from dimers formed by domain swapping between the alpha-crystallin domains,. adding to evidence that the smaller subunits are dimers. Here, the 2.5 angstrom resolution structure of an sHsp from the parasitic flatworm Taenia saginata Tsp36, the first metazoan crystal structure, shows a new mode of dimerization involving N-terminal regions, which differs from that seen for non-metazoan sHsps. Sequence differences in the a-crystallin domains between metazoans and nonmetazoans are critical to the different mechanism of dimerization, suggesting that some structural features seen for Tsp36 may be generalized to other metazoan sHsps. The structure also indicates scope for flexible assembly of subunits, supporting the proposed process of oligomer breakdown, substrate binding and reassembly as the chaperone mechanism. It further shows how sHsps can bind coil and secondary structural elements by wrapping them around the alpha-crystallin domain. The structure also illustrates possible roles for conserved residues associated with disease, and suggests a mechanism for the sHsp-related pathogenicity of some flatworm infections. Tsp36, like other flatworm sHsps, possesses two divergent sHsp repeats per monomer. Together with the two previously solved structures, a total of four alpha-crystallin domain structures are now available, giving a better definition of domain boundaries for sHsps

The HSP70 Molecular Chaperone Is Not Beneficial in a Mouse Model of α-synucleinopathy

BACKGROUND: Aggregation and misfolded alpha-synuclein is thought to be central in the pathogenesis of Parkinson's disease (PD). Heat-shock proteins (HSPs) that are involved in refolding and degradation processes could lower the aggregate load of alpha-synuclein and thus be beneficial in alpha-synucleinopathies. METHODOLOGY/PRINCIPAL FINDINGS: We co-overexpressed human A53T point-mutated alpha-synuclein and human HSP70 in mice, both under the control of Thy1 regulatory sequences. Behavior read-outs showed no beneficial effect of HSP70 expression in mice. In contrast, motor coordination, grip strength and weight were even worse in the alpha-synucleinopathy model in the presence of HSP70 overexpression. Biochemical analyses revealed no differences in alpha-synuclein oligomers/aggregates, truncations and phosphorylation levels and alpha-synuclein localization was unchanged in immunostainings. CONCLUSION/SIGNIFICANCE: Overexpressing HSP70 in a mouse model of alpha-synucleinopathy did not lower the toxic load of alpha-synuclein species and had no beneficial effect on alpha-synuclein-related motor deficits

Behavioral Defects in Chaperone-Deficient Alzheimer's Disease Model Mice

Molecular chaperones protect cells from the deleterious effects of protein misfolding and aggregation. Neurotoxicity of amyloid-beta (Aβ) aggregates and their deposition in senile plaques are hallmarks of Alzheimer's disease (AD). We observed that the overall content of αB-crystallin, a small heat shock protein molecular chaperone, decreased in AD model mice in an age-dependent manner. We hypothesized that αB-crystallin protects cells against Aβ toxicity. To test this, we crossed αB-crystallin/HspB2 deficient (CRYAB-/-HSPB2-/-) mice with AD model transgenic mice expressing mutant human amyloid precursor protein. Transgenic and non-transgenic mice in chaperone-sufficient or deficient backgrounds were examined for representative behavioral paradigms for locomotion and memory network functions: (i) spatial orientation and locomotion was monitored by open field test; (ii) sequential organization and associative learning was monitored by fear conditioning; and (iii) evoked behavioral response was tested by hot plate method. Interestingly, αB-crystallin/HspB2 deficient transgenic mice were severely impaired in locomotion compared to each genetic model separately. Our results highlight a synergistic effect of combining chaperone deficiency in a transgenic mouse model for AD underscoring an important role for chaperones in protein misfolding diseases

Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review

Mapping with surveying equipment is a time-consuming and cost-intensive procedure thatmakes the frequent map updating unaffordable. In the last few years, much research has focused oneliminating such problems by counting on crowdsourced data, such as GPS traces. An importantsource of information in maps, especially under the consideration of forthcoming self-driving vehicles,is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) andthis article is motivated by this fact. The topic of this systematic literature review (SLR) is the detectionand recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right ofway priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourceddata. More particularly, the aim of this study is (1) to identify the range of detected and recognisedregulatory types bycrowdsensingmeans, (2) to indicate the different classification techniques thatcan be used for these two tasks, (3) to assess the performance of different methods, as well as (4)to identify important aspects of the applicability of these methods. The two largest databases ofpeer-reviewed literature were used to locate relevant research studies and after different screeningsteps eleven articles were selected for review. Two major findings were concluded—(a) most regulatortypes can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) underthe current progress on the field, no study can be reproduced for comparative purposes nor can solelyrely on open data sources due to lack of publicly available datasets and ground truth maps. Futureresearch directions are highlighted as possible extensions of the reviewed studies