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

Comparison of direct and indirect methods for minimum lap time optimal control problems

Minimum lap time simulations are especially important in the design, optimisation and setup of race vehicles. Such problems usually come in different flavours, e.g. quasi-steady state models vs full dynamic models and pre-defined (fixed) trajectory problems vs free trajectory problems. This work is focused on full dynamic models with free trajectory. Practical solution techniques include direct methods (i.e. solution of an NLP problem, widespread approach) and indirect method (i.e. based on Pontryagins principle, less common, yet quite efficient in some cases). In this contribution the performance of the direct and indirect methods are compared in a number of vehicle related problems

Proceedings of IMECE

ABSTRACT In the last years a great effort has been devoted to the development of autonomous vehicles able to drive in a high range of speeds in semi-structured and unstructured environments. This article presents and discusses the software framework for Hardware-In-the-Loop (HIL) and Software-In-the-Loop (SIL) analysis that has been designed for developing and testing of control laws and mission functionalities of semi-autonomous and autonomous vehicles. The ultimate goal of this project is to develop a robotic system, named RUMBy, able to autonomously plan and execute accurate optimal manoeuvres both in normal and in critical driving situations and to be used as a test platform for advanced decision and autonomous driving algorithms. RUMBy's hardware is a 1:6 scale gasoline engine R/C car with onboard telemetry and control systems. RUMBy's software consists of three main modules: the manager module that coordinates the other modules and take high level decision; the motion planner module which is based on a Nonlinear Receding Horizon Control (NRHC) algorithm; the actuation module that produces the driving command for the vehicle. The article describes the details of RUMBy architecture and discusses its modular configuration that easily allows HIL and SIL tests

Estimating an individual's oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study

Measurement of oxygen uptake during exercise ([Formula: see text]) is currently non-accessible to most individuals without expensive and invasive equipment. The goal of this pilot study was to estimate cycling [Formula: see text] from easy-to-obtain inputs, such as heart rate, mechanical power output, cadence and respiratory frequency. To this end, a recurrent neural network was trained from laboratory cycling data to predict [Formula: see text] values. Data were collected on 7 amateur cyclists during a graded exercise test, two arbitrary protocols (Prot-1 and -2) and an "all-out" Wingate test. In Trial-1, a neural network was trained with data from a graded exercise test, Prot-1 and Wingate, before being tested against Prot-2. In Trial-2, a neural network was trained using data from the graded exercise test, Prot-1 and 2, before being tested against the Wingate test. Two analytical models (Models 1 and 2) were used to compare the predictive performance of the neural network. Predictive performance of the neural network was high during both Trial-1 (MAE = 229(35) mlO2min-1, r = 0.94) and Trial-2 (MAE = 304(150) mlO2min-1, r = 0.89). As expected, the predictive ability of Models 1 and 2 deteriorated from Trial-1 to Trial-2. Results suggest that recurrent neural networks have the potential to predict the individual [Formula: see text] response from easy-to-obtain inputs across a wide range of cycling intensities

Battery Aging-Aware Online Optimal Control: An Energy Management System for Hybrid Electric Vehicles Supported by a Bio-Inspired Velocity Prediction

In this manuscript, we address the problem of online optimal control for torque splitting in hybrid electric vehicles that minimises fuel consumption and preserves battery life. We divide the problem into the prediction of the future velocity profile (i.e. driver intention estimation) and the online optimal control of the hybrid powertrain following a Model Predictive Control (MPC) scheme. The velocity prediction is based on a bio-inspired driver model, which is compared on various datasets with two alternative prediction algorithms adopted in the literature. The online optimal control problem addresses both the fuel consumption and the preservation of the battery life using an equivalent cost given the estimated speed profile (i.e. guaranteeing the desired performance). The battery degradation is evaluated by means of a state-of-the-art electrochemical model. Both the predictor and the Energy Management System (EMS) are evaluated in simulation using real driving data divided into 30 driving cycles from 10 drivers characterised by different driving styles. A comparison of the EMS performances is carried out on two different benchmarks based on an offline optimization, in one case on the entire dataset length and in the second on an ideal prediction using two different receding horizon lengths. The proposed online system, composed of the velocity prediction algorithm and the optimal control MPC scheme, shows comparable performances with the previous ideal benchmarks in terms of fuel consumption and battery life preservation. The simulations show that the online approach is able to significantly reduce the capacity loss of the battery, while preserving the fuel saving performances

Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems

This position paper introduces the concept of artificial “co-drivers” as an enabling technology for future intelligent transportation systems. In Sections I and II, the design principles of co-drivers are introduced and framed within general human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and the emulation theory of cognition. In Sections III and IV, we present the co-driver developed for the EU project interactIVe as an example instantiation of this notion, demonstrating how it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V, we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research

Design, Development, Testing at ISO standards and in-vivo feasibility study of a novel Polymeric Heart Valve Prosthesis

Clinically available prosthetic heart valves are life-saving, but imperfect: mechanical valves requiring anticoagulation therapy, whilst bioprosthetic valves have limited durability. Polymer valves offer the prospect of good durability without the need for anticoagulation. We report the design and development of a polymeric heart valve, its bench-testing at ISO standards, and preliminary extravivo and in-vivo short-term feasibility. Prototypes were manufactured by injection moulding of styrenic block copolymers to achieve anisotropic mechanical properties. Design was by finite element stress-strain modelling, which has been reported previously, combined with feedback from bench and surgery-based testing using various combinations of materials, valve geometry and processing conditions. Bench testing was according to ISO 5840:2015 standards using an in-vitro cardiovascular hydrodynamic testing system and an accelerated fatigue tester. Bench comparisons were made with a best-in-class bio-prosthesis. Preliminary clinical feasibility evaluations included extra-vivo and short-term (1-24 hours) in-vivo testing in a sheep model. The optimised final prototype met the requirements of ISO standards with hydrodynamic performance equivalent to the best-in-class bioprosthesis. Bench durability of greater than 1.2 billion cycles (30 years equivalent) was achieved (still ongoing). Extra-vivo sequential testing (n=8) allowed refinement of external diameter, 3D shape, a low profile, flexibility, suturability, and testing of compatibility to magnetic resonance imaging and clinical sterilisation. In vivo short-term (1-24 hours) feasibility (n=3) confirmed good suturability, no mechanical failure, no trans-valvular regurgitation, competitive trans-valvular gradients, and good biocompatibility at histopathology. We have developed and tested at ISO standards a novel prosthetic heart valve featuring competitive bench-based hydrodynamics and durability, well beyond the ISO requirements and comparable to a best-in-class bioprosthesis. In-vivo short-term feasibility testing confirmed preliminary safety, functionality and biocompatibility, supporting progression to a long-term efficacy trial.King's College, Cambridg

Symbolic-Numeric Efficient Solution of Optimal Control Problems for Multibody Systems

This paper presents an efficient symbolic-numerical approach for generating and solving the Boundary Value Problem - Differential Algebraic Equation (BVP-DAE) originating from the variational form of the Optimal Control Problem (OCP). This paper presents the Method for the symbolic derivation, by means of symbolic manipulation software (Maple), of the equations of the OCP applied to a generic multibody system. The constrained problem is transformed into a non-constrained problem, by means of the Lagrange multipliers and penalty functions. From the first variation of the nonconstrained problem a BVP-DAE is obtained, and the finite difference discretization yields a non-linear systems. For the numerical solution of the non-linear system a damped Newton scheme is used. The sparse and structured jacobians is quickly inverted by exploiting the sparsity pattern in the solution strategy. The proposed method is implemented in an object oriented fashion, and coded in C++ language. Efficiency is ensured in core routines by using Lapack and Blas for linear algebra

Experimental evaluation of a system for assisting motorcyclists to safely ride road bends

Introduction: Road bends of extra-urban and rural roads are known to be particularly relevant for motorcycle riding safety. For this reason a Curve Warning system has been developed for assisting the motorcyclists to safely approach bends and curves.System Description: The system is organized in three layers: the first is the scenario detection that uses on-board sensors and digital maps to feed the second layer, which is the risk assessment layer. This second layer combines road geometry, motorcycle dynamics, and rider style in a holistic approach for computing a safe reference maneuver and for detecting potential dangers in the curve negotiation. The safe reference maneuver is continuously recalculated to follow the evolving scenario according to a receding horizon approach. In case of potential danger, the third layer warns the rider by a proper Human Machine Interface, leaving to the rider the vehicle control.Paper contents: This paper explains the Curve Warning concept and illustrates its implementation, development, and tuning on a motorcycle prototype. The latter has been used for a pilot campaign of road tests, which demonstrated that the system is capable of early detection of potential danger situations, and that riders have a positive attitude towards the Curve Warning system itself.<br/