A/C Energy Management and Vehicle Cabin Thermal Comfort Control

This paper introduces a novel multi-objective controller which regulates A/C system operation in a trade-off between vehicle cabin comfort and fuel consumption for a conventional vehicle with internal combustion engine. The controller has been developed and tested in a simulated environment, where an energy-based model of the A/C system is combined with a thermal dynamic model of the cabin which considers heat transfer to the environment. The control algorithm proposed herein is compared with two widely used control techniques in the industry, respectively the thermostat and PI control, under different driving cycles. This novel method is implementable in real-time, and simulation results show a reduction of up to 2% in A/C system fuel consumption compared to existing methods with similar thermal performance

Adaptive driver modelling in ADAS to improve user acceptance: A study using naturalistic data

Accurate understanding of driver behaviour is crucial for future Advanced Driver Assistance Systems (ADAS) and autonomous driving. For user acceptance it is important that ADAS respect individual driving styles and adapt accordingly. Using data collected during a naturalistic driving study carried out at the University of Southampton, we assess existing models of driver acceleration and speed choice during car following and when cornering. We observe that existing models of driver behaviour that specify a preferred inter-vehicle spacing in car-following situations appear to be too prescriptive, with a wide range of acceptable spacings visible in the naturalistic data. Bounds on lateral acceleration during cornering from the literature are visible in the data, but appear to be influenced by the minimum cornering radii specified in design codes for UK roadway geometry. This analysis of existing driver models is used to suggest a small set of parameters that are sufficient to characterise driver behaviour in car-following and curve driving, which may be estimated in real-time by an ADAS to adapt to changing driver behaviour. Finally, we discuss applications to adaptive ADAS with the objectives of improving road safety and promoting eco-driving, and suggest directions for future researc

Fitting cornering speed models with one-class support vector machines

© 2019 IEEE. This paper investigates the modelling of cornering speed using road curvature as a predictive variable, which is of interest for advanced driver assistance system (ADAS) applications including eco-driving assistance and curve warning. Such models are common in the driver modelling and human factors literature, yet lack reliable parameter estimation methods, requiring an ad-hoc evaluation of the upper envelope of the data followed by linear regression to that envelope. Considering the space of possible combinations of lateral acceleration and cornering speed, we cast the modelling of cornering speed as an 'outlier detection' problem which may be solved using one-class Support Vector Machine (SVM) methods from machine learning. For an existing cornering model, we suggest a fitting method using a specific choice of kernel function in a one-class SVM. As the parameters of the cornering speed model may be recovered from the SVM solution, this provides a more robust and reproducible fitting method for this model of cornering speed than the existing envelope-based approaches. In addition, this gives comparable outlier detection performance to generic SVM methods based on Radial Basis Function (RBF) kernels while reducing training times by a factor of 10, indicating potential for use in adaptive eco-driving assistance systems that require retraining either online or between drives

Incorporating driver preferences Into eco-driving assistance systems using optimal control

Recently there have been several proposals for ‘ecodriving assistance systems’, designed to save fuel or electrical power by encouraging behaviours such as gentle acceleration and coasting to a stop. These systems use optimal control to find driving behaviour that minimises vehicle energy losses. In this paper, we introduce a methodology to account for driver preferences on acceleration, braking, following distances and cornering speed in such eco-driving optimal control problems. This consists of an optimal control model of acceleration and braking behaviour containing several physically-meaningful parameters to describe driver preferences. If used in combination with a model of fuel or energy consumption, this can provide an adjustable trade-off between satisfying those preferences and minimising energy losses. We demonstrate that the model gives comparable performance to existing car-following and cornering models when predicting drivers’ speed in these situations by comparison with real-world driving data. Finally, we present an example highway braking scenario for an electric vehicle, illustrating a trade-off between satisfying driver preferences on vehicle speed and acceleration and reducing electrical energy usage by up to 43%</div

An Integrated GNSS/LiDAR-SLAM Pose Estimation Framework for Large-Scale Map Building in Partially GNSS-Denied Environments

This article presents an integrated global navigation satellite system/light detection and ranging (GNSS/LiDAR)-based simultaneous localization and mapping (SLAM) pose estimation framework to perform large-scale 3-D map building in partially GNSS-denied outdoor environments. The framework takes the advantage of the complementarity between GNSS positioning and LiDAR-SLAM to decompose the map building task according to the GNSS real-time kinematic (RTK) status. When mapping in GNSS-denied scenes, a 3-D LiDAR-SLAM algorithm is adopted to estimate poses and a correction algorithm is presented to correct drift errors. On the other hand, when mapping in open scenes, a GNSS-initialized LiDAR mapping algorithm (GL-mapping) is proposed to loosely couple GNSS positioning and LiDAR data registration. It can perform the orientation estimation without the use of either the high-cost inertial sensing device or the GNSS dual-antenna. Experiments are conducted in large-scale outdoor environments to demonstrate that the proposed framework can accomplish simultaneous pose estimation and map building with high precision in both open scenes and GNSS-denied scenes

Adaptive driver modelling in ADAS to improve user acceptance: a study using naturalistic data

Accurate understanding of driver behaviour is crucial for future Advanced Driver Assistance Systems (ADAS) and autonomous driving. For user acceptance it is important that ADAS respect individual driving styles and adapt accordingly. Using data collected during a naturalistic driving study carried out at the University of Southampton, we assess existing models of driver acceleration and speed choice during car following and when cornering. We observe that existing models of driver behaviour that specify a preferred inter-vehicle spacing in car-following situations appear to be too prescriptive, with a wide range of acceptable spacings visible in the naturalistic data. Bounds on lateral acceleration during cornering from the literature are visible in the data, but appear to be influenced by the minimum cornering radii specified in design codes for UK roadway geometry. This analysis of existing driver models is used to suggest a small set of parameters that are sufficient to characterise driver behaviour in car-following and curve driving, which may be estimated in real-time by an ADAS to adapt to changing driver behaviour. Finally, we discuss applications to adaptive ADAS with the objectives of improving road safety and promoting eco-driving, and suggest directions for future research

PtdIns (3,4,5) P3 Recruitment of Myo10 Is Essential for Axon Development

Myosin X (Myo10) with pleckstrin homology (PH) domains is a motor protein acting in filopodium initiation and extension. However, its potential role has not been fully understood, especially in neuronal development. In the present study the preferential accumulation of Myo10 in axon tips has been revealed in primary culture of hippocampal neurons with the aid of immunofluorescence from anti-Myo10 antibody in combination with anti-Tuj1 antibody as specific marker. Knocking down Myo10 gene transcription impaired outgrowth of axon with loss of Tau-1-positive phenotype. Interestingly, inhibition of actin polymerization by cytochalasin D rescued the defect of axon outgrowth. Furthermore, ectopic expression of Myo10 with enhanced green fluorescence protein (EGFP) labeled Myo10 mutants induced multiple axon-like neurites in a motor-independent way. Mechanism studies demonstrated that the recruitment of Myo10 through its PH domain to phosphatidylinositol (3,4,5)-trisphosphate (PtdIns (3,4,5) P3) was essential for axon formation. In addition, in vivo studies confirmed that Myo10 was required for neuronal morphological transition during radial neuronal migration in the developmental neocortex

Hybrid modelling and control for switched-mode power converters

Switched-mode power converters are some of the most widely used power electronics circuits due to their advantages of high conversion efficiency, flexible output voltage, light weight. A variety of control methods have been developed for the switched-mode power converters. However, in many practical situation, additional constraints need to be considered, e.g., safety measurement, current limiting or soft-starting, gross changes of operation point with guaranteed system stability, which has not been fully addressed in the available research works. On the other hand, the majority of the control design for power converters are based on the state-space averaged approach which involves considerable approximation in analysis and synthesis. Hence, advanced control techniques are in demand, which should be more constraints friendly and based on more precise models.In this thesis, much attention has been spent on designing controllers for both DC-DC converters and DC-AC inverters based on hybrid modelling and Lyapunov stability theory. Due to the existence of the power switches, switched-mode power converters are hybrid systems with both continuous dynamics and discrete transition events. Instead of linearizing the converter model around a specific operating point, hybrid modelling captures both dynamics, which results in more accurate models.Firstly, a novel sampled-data control approach is proposed for DC-DC converters. DC-DC converters are modeled as sampled-data switched affine systems according to the status of the power switch. In order to avoid the delay of the switching signal, an on-line prediction method is adopted to estimate the system state at the next switching instant. Based on the switched affine model and the predicted system state, a novel switching control algorithm is synthesized by using the switched Lyapunov theory. The proposed approach is able to not only drive the output to a prescribed set point from any initial condition, but also track a varying reference signal, and the switching frequency can be adjusted online with guaranteed stability. In addition, with this approach, Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM) operations can be treated in a unified way. Experimental verification has been carried out to test the effectiveness and merits of the proposed method.Furthermore, to compensate the information loss due to limited access to the state, a multiple sampling scheme is employed to derive a discrete-time switched affine model with an augmented measurement output for DC-DC converters. Based on the model, an output-feedback switching control law, which drives the system state to a set of attainable switched equilibria, is synthesized by using a quadratic state-space partition. The multiple sampling scheme not only facilitates the controller synthesis, but also improves the energy efficiency of the converter by allowing a lower switching frequency.In addition, hybrid modelling techniques have been extended to more complicated cases – DC-AC inverters as the increasing number of power switches and the time-variant nature of the references. A current controller based on the hybrid model of the three-phase two-level inverter has been developed, which can drive the inverter currents tracking the desired power references in realtime and keep a unity power factor at the same time. This method has been extended to three-phase NPC inverters later on. However, in order to solve the neutral point balancing issue, a capacitor voltages prediction algorithm, modified from model predictive control, has been adopted. It should also be mentioned that a novel hybrid model for a grid-connected single-phase NPC inverter also has been presented, which models not only the dynamic of the inverter but also the dynamic of the current reference. An experimental test platform including a three-phase NPC inverter and a FPGA control board has been designed to demonstrate the implementation of the proposed control scheme in practice

Fuel economy and naturalistic driving for passenger road vehicles

The state-of-the-art eco-driving techniques does not take into account the naturalistic behaviour of human drivers. Therefore, in this paper, a unified driver model is proposed which describes the driver preference during car following and cornering cases. The model is formulated based on the optimal control theory. The fuel consumption model of a traditional vehicle with an internal combustion (IC) engine and CVT transmission is combined with the driver model. The proposed optimal controller is designed to generate speed profile and powertrain inputs, which gives a compromise between the driver preference and fuel economy. The simulation results demonstrate that eco-friendly speed profile and optimal powertrain input trajectories could be selected which has good fuel economy and matches the driver desires.</p