To address the problem of estimating camera trajectory and to build a structural three-dimensional (3D) map based on inertial measurements and visual observations, this paper proposes point–line visual–inertial odometry (PL-VIO), a tightly-coupled monocular visual–inertial odometry system exploiting both point and line features. Compared with point features, lines provide significantly more geometrical structure information on the environment. To obtain both computation simplicity and representational compactness of a 3D spatial line, Plücker coordinates and orthonormal representation for the line are employed. To tightly and efficiently fuse the information from inertial measurement units (IMUs) and visual sensors, we optimize the states by minimizing a cost function which combines the pre-integrated IMU error term together with the point and line re-projection error terms in a sliding window optimization framework. The experiments evaluated on public datasets demonstrate that the PL-VIO method that combines point and line features outperforms several state-of-the-art VIO systems which use point features only.

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PurposeThis paper presents a novel hands‐free control system for intelligent wheelchairs (IWs) based on visual recognition of head gestures.Design/methodology/approachA robust head gesture‐based interface (HGI), is designed for head gesture recognition of the RoboChair user. The recognised gestures are used to generate motion control commands to the low‐level DSP motion controller so that it can control the motion of the RoboChair according to the user's intention. Adaboost face detection algorithm and Camshift object tracking algorithm are combined in our system to achieve accurate face detection, tracking and gesture recognition in real time. It is intended to be used as a human‐friendly interface for elderly and disabled people to operate our intelligent wheelchair using their head gestures rather than their hands.FindingsThis is an extremely useful system for the users who have restricted limb movements caused by some diseases such as Parkinson's disease and quadriplegics.Practical implicationsIn this paper, a novel integrated approach to real‐time face detection, tracking and gesture recognition is proposed, namely HGI.Originality/valueIt is an useful human‐robot interface for IWs.

In freeways, the capacity drop means that the maximum traffic flow is higher than congestion discharge rates there. Various capacity drop magnitudes have been empirically observed before. But the mechanism behind this wide capacity drop range is not yet found. This contribution fills in the gap by relating the congestion discharge rates to different congestions in empirical observations. Two days' data show that the outflows of stopand-go waves are always lower than those of standing queues. Different discharge rates, ranging from 5220 to 6040 veh/h at the same site, always accompany different congestion states. Moreover, the different observations show that a higher discharge rate means a higher density in the free-flow branch in the fundamental diagram. This contribution shows that discharging rates probably could be controlled by transforming the congestion states. For instance, transforming a stop-and-go wave into a standing queue at a bottleneck might increase the bottleneck throughput.

A geometric Brownian motion car-following model: towards a better understanding of capacity drop, Transportmetrica B: Transport Dynamics, 7:1, 915-927, ABSTRACTTraffic flow downstream of the congestion is generally lower than the pre-queue capacity. This phenomenon is called the capacity drop. Recent empirical observations show a positive relationship between the speed in congestion and the queue discharge rate. Literature indicates that variations in driver behaviors can account for the capacity drop. However, to the best of authors' knowledge, there is no solid understanding of what and how this variation in driver behaviors lead to the capacity drop, especially without lane changing. Hence, this paper fills this gap. We incorporate the empirically observed desired acceleration stochasticity into a car-following model. The extended parsimonious car-following model shows different capacity drop magnitudes in different traffic situations, consistent with empirical observations. All results indicate that the stochasticity of desired accelerations is a significant reason for the capacity drop. The new insights can be used to develop and test new measures in traffic control. ARTICLE HISTORY

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Regulatory changes are transforming the multitrillion dollar swaps market from a network of bilateral contracts to one in which swaps are cleared through central counterparties (CCPs). The stability of the new framework depends on the CCPs' resilience. Margin requirements are a CCP's first line of defense against the default of a counterparty. To capture liquidity costs at default, margin requirements need to increase superlinearly in position size. However, convex margin requirements create an incentive for a swaps dealer to split its positions across multiple CCPs, effectively "hiding" potential liquidation costs from each CCP. To compensate, each CCP needs to set higher margin requirements than it would in isolation. In a model with two CCPs, we define an equilibrium as a pair of margin schedules through which both CCPs collect sufficient margin under a dealer's optimal allocation of trades. In the case of linear price impact, we show that a necessary and sufficient condition for the existence of an equilibrium is that the two CCPs agree on liquidity costs, and we characterize all equilibria when this holds. A difference in views can lead to a race to the bottom. We provide extensions of this result and discuss its implications for CCP oversight and risk management.