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

An intelligent curve warning system for powered two wheel vehicles

This article illustrates a novel Curve Warning system for motorcycles which has been developed in the SAFERIDER project (www.saferider-eu.org) of the 7th EU FP, among other Advanced Rider Assistance Systems. The Curve Warning function (CW) described here follows a holistic approach, which combines road geometry, motorcycle dynamics, rider input and riding styles. The warning strategy is based on the correction of longitudinal dynamics derived from a previewed ideal manoeuvre (reference manoeuvre) continuously computed from the actual state of the vehicle. Under normal driving conditions the reference manoeuvre matches the rider's and no correction is needed and no warning is given. But if large differences between actual and ideal accelerations are found the rider is warned to decelerate or brake. As soon as the correct value of deceleration is achieved the warning disappears, improving system acceptability. Warnings are given to the rider via an HMI, which uses a haptic accelerator throttle, a vibrating glove and helmet, and a visual display

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

Formation-Based Odour Source Localisation Using Distributed Terrestrial and Marine Robotic Systems

This thesis tackles the problem of robotic odour source localisation, that is, the use of robots to find the source of a chemical release. As the odour travels away from the source, in the form of a plume carried by the wind or current, small scale turbulence causes it to separate into intermittent patches, suppressing any gradients and making this a particularly challenging search problem. We focus on distributed strategies for odour plume tracing in the air and in the water and look primarily at 2D scenarios, although novel results are also presented for 3D tracing. The common thread to our work is the use of multiple robots in formation, each outfitted with odour and flow sensing devices. By having more than one robot, we can gather observations at different locations, thus helping overcome the difficulties posed by the patchiness of the odour concentration. The flow (wind or current) direction is used to orient the formation and move the robots up-flow, while the measured concentrations are used to centre the robots in the plume and scale the formation to trace its limits. We propose two formation keeping methods. For terrestrial and surface robots equipped with relative or absolute positioning capabilities, we employ a graph-based formation controller using the well-known principle of Laplacian feedback. For underwater vehicles lacking such capabilities, we introduce an original controller for a leader-follower triangular formation using acoustic modems with ranging capabilities. The methods we propose underwent extensive experimental evaluation in high-fidelity simulations and real-world trials. The marine formation controller was implemented in MEDUSA autonomous vehicles and found to maintain a stable formation despite the multi-second ranging period. The airborne plume tracing algorithm was tested using compact Khepera robots in a wind tunnel, yielding low distance overheads and reduced tracing error. A combined approach for marine plume tracing was evaluated in simulation with promising results

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

Active safety systems for powered two-wheelers: A systematic review

Objective: Active safety systems, of which antilock braking is a prominent example, are going to play an important role to improve powered two-wheeler (PTW) safety. This paper presents a systematic review of the scientific literature on active safety for PTWs. The aim was to list all systems under development, identify knowledge gaps and recognize promising research areas that require further efforts. Methods: A broad search using "safety" as the main keyword was performed on Scopus, Web of Science and Google Scholar, followed by manual screening to identify eligible papers that underwent a full-text review. Finally, the selected papers were grouped by general technology type and analyzed via structured form to identify the following: specific active safety system, study type, outcome type, population/sample where applicable, and overall findings. Results: Of the 8,000 papers identified with the initial search, 85 were selected for full-text review and 62 were finally included in the study, of which 34 were journal papers. The general technology types identified included antilock braking system, autonomous emergency braking, collision avoidance, intersection support, intelligent transportation systems, curve warning, human machine interface systems, stability control, traction control, and vision assistance. Approximately one third of the studies considered the design and early stage testing of safety systems (n. 22); almost one fourth (n.15) included evaluations of system effectiveness. Conclusions: Our systematic review shows that a multiplicity of active safety systems for PTWs were examined in the scientific literature, but the levels of development are diverse. A few systems are currently available in the series production, whereas other systems are still at the level of early-stage prototypes. Safety benefit assessments were conducted for single systems, however, organized comparisons between systems that may inform the prioritization of future research are lacking. Another area of future analysis is on the combined effects of different safety systems, that may be capitalized for better performance and to maximize the safety impact of new technologies