Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation

Predicting the trajectories of surrounding agents is an essential ability for autonomous vehicles navigating through complex traffic scenes. The future trajectories of agents can be inferred using two important cues: the locations and past motion of agents, and the static scene structure. Due to the high variability in scene structure and agent configurations, prior work has employed the attention mechanism, applied separately to the scene and agent configuration to learn the most salient parts of both cues. However, the two cues are tightly linked. The agent configuration can inform what part of the scene is most relevant to prediction. The static scene in turn can help determine the relative influence of agents on each other's motion. Moreover, the distribution of future trajectories is multimodal, with modes corresponding to the agent's intent. The agent's intent also informs what part of the scene and agent configuration is relevant to prediction. We thus propose a novel approach applying multi-head attention by considering a joint representation of the static scene and surrounding agents. We use each attention head to generate a distinct future trajectory to address multimodality of future trajectories. Our model achieves state of the art results on the nuScenes prediction benchmark and generates diverse future trajectories compliant with scene structure and agent configuration.Comment: Revised submission for RA-

An Ontological Approach to Inform HMI Designs for Minimizing Driver Distractions with ADAS

ADAS (Advanced Driver Assistance Systems) are in-vehicle systems designed to enhance driving safety and efficiency as well as comfort for drivers in the driving process. Recent studies have noticed that when Human Machine Interface (HMI) is not designed properly, an ADAS can cause distraction which would affect its usage and even lead to safety issues. Current understanding of these issues is limited to the context-dependent nature of such systems. This paper reports the development of a holistic conceptualisation of how drivers interact with ADAS and how such interaction could lead to potential distraction. This is done taking an ontological approach to contextualise the potential distraction, driving tasks and user interactions centred on the use of ADAS. Example scenarios are also given to demonstrate how the developed ontology can be used to deduce rules for identifying distraction from ADAS and informing future designs

Multi-synaptic boutons are a feature of CA1 hippocampal connections in the stratum oriens

Excitatory synapses are typically described as single synaptic boutons (SSBs), where one presynaptic bouton contacts a single postsynaptic spine. Using serial section block-face scanning electron microscopy, we found that this textbook definition of the synapse does not fully apply to the CA1 region of the hippocampus. Roughly half of all excitatory synapses in the stratum oriens involved multi-synaptic boutons (MSBs), where a single presynaptic bouton containing multiple active zones contacted many postsynaptic spines (from 2 to 7) on the basal dendrites of different cells. The fraction of MSBs increased during development (from postnatal day 22 [P22] to P100) and decreased with distance from the soma. Curiously, synaptic properties such as active zone (AZ) or postsynaptic density (PSD) size exhibited less within-MSB variation when compared with neighboring SSBs, features that were confirmed by super-resolution light microscopy. Computer simulations suggest that these properties favor synchronous activity in CA1 networks

Low Speed Automation, a French Initiative

Nowadays, vehicle safety is constantly increasing thanks to the improvement of vehicle passive and active safety. However, on a daily usage of the car, traffic jams remains a problem. With limited space for road infrastructure, automation of the driving task on specific situation seems to be a possible solution. The French project ABV, which stands for low speed automation, tries to demonstrate the feasibility of the concept and to prove the benefits. In this article, we describe the scientific background of the project and expected outputs

Wastewater characteristics in Palestine

Wastewater treatment plants in Palestine (West Bank and Gaza Strip) have been designed upon assumptions of wastewater characteristics and amount of flow, because no data were available at all. This study is focused on the collection and measurement of wastewater quantity and quality, which will be used as a basis for formulating a strategic plan for wastewater treatment in Palestine. In view of the limited water resources, reclaimed wastewater will be used for agriculture, which, at present, is done at a very limited scale. The infrastructure in Palestine used to be a neglected issue, particularly with regard to sewerage. Blocked pipes and flooded manholes are daily events especially in Gaza Strip. All existing treatment plants are heavily overloaded. Groundwater resources in Gaza Strip become more and more polluted with nitrate concentrations exceeding 100 mg NO3-N/l resulting from seepage from cesspits and effluent discharges into wadis. Due to scarcity of water, domestic water consumption is very low, which leads to highly concentrated wastewater similar to industrial one. In one city in the West Bank, the COD reached a value of 3,670 mg/l, although all the water reaching the outfall is of domestic type.</jats:p