A normal driving based deceleration behaviour study towards autonomous vehicles

Vehicle automation has recently attracted significant interest from the research community worldwide. Notwithstanding the remarkable development in autonomous vehicles (AVs), there is still a concern about the occupant's comfort since most research has mainly focused on the safety aspect. One of the most critical factors affecting the comfort level is the braking. It is however unclear which factors affect the braking behaviour and which braking profiles make the occupants feel safe and comfortable. This work therefore aims to thoroughly explore the deceleration behaviour of drivers using naturalistic driving study (NDS) data from two Field Operational Tests (FOT), the Pan-European TeleFOT (Field Operational Tests of Aftermarket and Nomadic Devices in Vehicles) project and the FOT conducted by Loughborough University and Nissan Ltd. A total of about 28 million observations were examined and almost 3,000 deceleration events from 37 different drivers and 174 different trips were identified and analysed. With the aid of a cluster analysis, a number of homogeneous scenarios based on human factors were formed. The scenarios have led to the application of multilevel mixed effect linear models to each cluster examining all influencing factors of the braking behaviour. The results indicate a dependence of the deceleration behaviour differing due to driver characteristics, initial speed and the reason for braking. Findings from this study will support vehicle manufacturers to ensure comfortable and safe braking operations of AV

Energy management in electric vehicles: Development and validation of an optimal driving strategy

Electric vehicles (EVs) are a promising alternative energy mode of transportation for the future. However, due to the limited range and relatively long charging time, it is important to use the stored battery energy in the most optimal manner possible. Existing research has focused on improvements to the hardware or improvements to the energy management strategy (EMS). However, EV drivers may adopt a driving strategy that causes the EMS to operate the EV hardware in inefficient regimes just to fulfil the driver demand. The present study develops an optimal driving strategy to help an EV driver choose a driving strategy that uses the stored battery energy in the most optimal manner. First, a strategy to inform the driver about his/her current driving situation is developed. Then, two separate multi-objective strategies, one to choose an optimal trip speed and another to choose an optimal acceleration strategy, are presented. Finally, validation of the optimal driving strategy is presented for a fleet-style electric bus. The results indicated that adopting the proposed approach could reduce the electric bus’ energy consumption from about 1 kWh/mile to 0.6-0.7 kWh/mile. Optimization results for a fixed route around the Missouri S&T campus indicated that the energy consumption of the electric bus could be reduced by about 5.6% for a 13.9% increase in the trip time. The main advantage of the proposed strategy is that it reduces the energy consumption while minimally increasing the trip time. Other advantages are that it allows the driver flexibility in choosing trip parameters and it is fairly easy to implement without significant changes to existing EV designs. --Abstract, page iii

Modelling drivers’ braking behaviour and comfort under normal driving

The increasing growth of population and a rising number of vehicles, connected to an individual, demand new solutions to reduce traffic delays and enhance road safety. Autonomous Vehicles (AVs) have been considered as an optimal solution to overcome those problems. Despite the remarkable research and development progress in the area of (semi) AVs over the last decades, there is still concern that occupants may not feel safe and comfortable due to the robot-like driving behaviour of the current technology. In order to facilitate their rapid uptake and market penetration, ride comfort in AVs must be ensured.Braking behaviour has been identified to be a crucial factor in ride comfort. There is a dearth of research on which factors affect the braking behaviour and the comfort level while braking and which braking profiles make the occupants feel safe and comfortable. Therefore, the primary aim of this thesis is to model the deceleration events of drivers under normal driving conditions to guide comfortable braking design. The aim was achieved by exploiting naturalistic driving data from three projects: (1) the Pan-European TeleFOT (Field Operational Tests of Aftermarket and Nomadic Devices in Vehicles) project, (2) the Field Operational Test (FOT) conducted by Loughborough University and Original Equipment Manufacturer (OEM), and (3) the UDRIVE Naturalistic Driving Study.A total of about 35 million observations were examined from 86 different drivers and 644 different trips resulting in almost 10,000 deceleration events for the braking features analysis and 21,600 deceleration events for the comfort level analysis. Since deceleration events are nested within trips and trips within drivers, multilevel mixed-effects linear models were employed to develop relationships between deceleration value and duration and the factors influencing them. The examined factors were kinematics, situational, driver and trip characteristics with the first two categories to affect the most the deceleration features. More specifically, the initial speed and the reason for braking play a significant role, whereas the driver’s characteristics, i.e. the age and gender do not affect the deceleration features, except for driver’s experience which significantly affects the deceleration duration.An algorithm was developed to calculate the braking profiles, indicating that the most used profile follows smooth braking at the beginning followed by a harder one. Moreover, comfort levels of drivers were analysed using the Mixed Multinomial Logit models to identify the effect of the explanatory factors on the comfort category of braking events. Kinematic factors and especially TTC and time headway (THW) were found to affect the most the comfort level. Particularly, when TTC or THW are increased by 1 second, the odds of the event to be “very comfortable” are respectively 1.03 and 4.5 times higher than being “very uncomfortable”. Moreover, the driver’s characteristic, i.e. age and gender affect significantly the comfort level of the deceleration event. Findings from this thesis can support vehicle manufacturers to ensure comfortable and safe braking operations of AVs.</div

License to Supervise:Influence of Driving Automation on Driver Licensing

To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects drivers’ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation

Multiobjective Gear Shifting Optimization Considering A Known Driving Cycle [otimização Multiobjetivo Da Troca De Marchas Em Um Ciclo De Condução Previamente Conhecido]

The reduction of the fuel consumption of the vehicles driven by combustion engines is a target of the automakers, governments and drivers. The literature asserts that the adjustment of the driver behavior results in a substantial fuel economy. Specifically, the gear shifting is one aspect of the driver behavior that can be changed by the use of support systems installed in the vehicle that indicate the right moment that the gear must be shifted. The interested community is focused on the development of the algorithms that are implemented in these support systems. These algorithms must be able to arbitrate between two antagonistic objective functions simultaneously: the maximization of the performance and the fuel economy. Thus, this paper demonstrates that it is possible to calculate the trade-off threshold between performance and fuel economy of a vehicle by means of the multiobjective optimization of the gear shifting considering a known driving cycle. To reach this objective, it is created a dynamic model of an automobile base on the literature data; the optimization algorithm implemented is Non-dominated Sorting Genetic Algorithm - II and the driving cycle used is described by the standards ABNT NBR6601:2012 and FTP-72. © 2015, Eduem - Editora da Universidade Estadual de Maringa. All rights reserved.373361369Ala-Lawi, B.M., Bradley, T.H., Analysis of corporate average fuel economy regulation compliance scenarios inclusive of plug in hybrid vehicles (2014) Applied Energy, 113, pp. 1323-1337Alam, M.S., McNabola, A., A critical review and assessment of Eco-Driving policy & technology: Benefits & limitations (2014) Transport Policy, 35, pp. 42-49Bahn, O., Marcy, M., Vaillancourt, K., Waaub, J.P., Electrification of the Canadian road transportation sector: A 2050 outlook with TIMESCanada (2013) Energy Policy, 62, pp. 593-606Banos, R., Manza-Noagugliaro, F., Montoya, F.G., Gil, C., Alcayde, A., Gómez, J., Optimization methods applied to renewable and sustainable energy: A review (2011) Renewable and Sustainable Energy Reviews, 15 (4), pp. 1753-1766Deb, K., Multi-objective optimization (2014) Search Methodologies, pp. 403-449. , BURKE, E. K.KENDALL, G. (Ed.), New York: SpringerDovgan, E., Javorski, M., Tušar, T., Gams, M., Filipič, B., Comparing a multiobjective optimization algorithm for discovering driving strategies with humans (2013) Expert Systems with Applications, 40 (7), pp. 2687-2695Dovgan, E., Tušar, T., Javorski, M., Filipic, B., Discovering Comfortable Driving Strategies Using Simulation-Based Multiobjective Optimization (2012) Informatica, 36 (3), pp. 319-326Eckert, J.J., Corrêa, F.C., Santiciolli, F.M., Costa, E.S., Dionísio, H.J., Dedini, F.G., Gear Shifting Strategies Co-simulations to Optimize Vehicle Performance and Fuel Consumption (2015) Multibody Mechatronic Systems, pp. 143-152. , CECCARELLI. M.MARTINEZ, E. E. H, Cham: Springer International PublishingGenta, G., (1997) Motor Vehicle Dynamics: Modeling and Simulation, , Singapore: World ScientificGillespie, T.D., (1992) Fundamentals of Vehicle Dynamics, , Warrendale: Society of Automotive EngineersHa, S.H., Jeon, H.T., Development of Intelligent Gear-shifting Map Based on Radial Basis Function Neural Networks (2013) International Journal of Fuzzy Logic and Intelligent Systems, 13 (2), pp. 116-123Ho, S.H., Wong, Y.D., Chang, V., Developing Singapore Driving Cycle for passenger cars to estimate fuel consumption and vehicular emissions (2014) Atmospheric Environment, 97, pp. 353-362Jasion, G., Shrimpton, J., Danby, M., Takeda, K., Performance of numerical integrators on tangential motion of DEM within implicit flow solvers (2011) Computers and Chemical Engineering, 35 (11), pp. 2218-2226Ntziachristos, L., Mellios, G., Tsokolis, D., Keller, M., Hausberger, S., Ligterink, N.E., Dilara, P., In-use vs. Type-approval fuel consumption of current passenger cars in Europe (2014) Energy Policy, 67, pp. 403-411Thiel, C., Schmidt, J., Van Zyl, A., Schmid, E., Cost and well-to-wheel implications of the vehicle fleet CO2 emission regulation in the European Union (2014) Transportation Research Part A: Policy and Practice, 63, pp. 25-42Thijssen, R., Hofman, T., Ham, J., Ecodriving acceptance: An experimental study on anticipation behavior of truck drivers (2014) Transportation Research Part F: Traffic Psychology and Behaviour, 22, pp. 249-260Vagg, C., Brace, C.J., Hari, D., Akehurst, S., Poxon, J., Ash, L., Development and field trial of a driver assistance system to encourage eco-driving in light commercial vehicle fleets. Intelligent Transportation Systems (2013) IEEE Transactions On, 14 (2), pp. 796-80