Philipp Themann

Bio: Philipp Themann is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Fuel efficiency & Efficient energy use. The author has an hindex of 8, co-authored 20 publications receiving 157 citations.

Optimisation of energy efficiency based on average driving behaviour and driver's preferences for automated driving

Abstract: The implementation of anticipating driving styles in adaptive cruise control systems promises to considerably reduce fuel consumption of vehicles. As drivers have to accept the optimised driving styles of such systems, which implement longitudinally automated driving, the optimisation results should not deviate strongly from the average driving behaviour. This work presents an approach to the optimisation of the vehicle's longitudinal dynamics, which is based on a predicted average driving profile. The proposed approach ensures that the optimisation results meet the expectations of drivers by directly accounting for driver's preferences on weighting up travel time against fuel consumption relative to the average driving profile. Based on human decision finding, rational and intuitive planning decisions are modelled in a cost function and represent optimisation constraints. The approach generally includes information from vehicle-to-vehicle and vehicle-to-infrastructure communication (V2X), which is an extension to the state-of-the-art. This study describes the optimisation approach and presents a method to determine suitable optimisation parameters in order to consider driver's preferences. The optimisation approach is applied in a simulated test drive and improvements in fuel economy are analysed. Finally, the authors sketch a reference system architecture to prove the feasibility of the presented approach.

Impact of positioning uncertainty of vulnerable road users on risk minimization in collision avoidance systems

Abstract: This work describes a methodology to assess the impact of positioning and prediction accuracy on the potential benefit of collision avoidance systems. The predicted position of vulnerable road users (VRU) ahead of the vehicle is affected by measurement and prediction uncertainty. In advanced cooperative collision avoidance systems the position of VRUs is provided by vehicle-to-vehicle or vehicle-to-infrastructure (V2X) communication. This work describes a method to optimize the vehicle's longitudinal and lateral trajectories in critical situations in order to minimize the risk of the situation considering the influence of positioning and prediction inaccuracies of VRU. The findings discussed here define requirements on the prediction accuracy and for vehicle velocities of 50 km/h the predicted VRU position should provide a standard deviation of less than 55 cm.

Optimal Control of Series Plug-In Hybrid Electric Vehicles Considering the Cabin Heat Demand

Abstract: In series plug-in hybrid electric vehicles, the engine is decoupled from the wheels and the fuel economy is not very sensitive to the energy management. Therefore, different works recommend charge depletion, charge sustenance (CDCS) strategies for vehicle implementation as they always ensure a desirable full exploitation of the battery capacity. In contrast, this brief illustrates great fuel saving potentials by blending CD and CS with regard to using the engine waste heat for cabin heating. In this way, the energy demand of the electric heater and thus the fuel consumption are reduced significantly. The potential is outlined by comparing the fuel consumption of optimal blended and optimal CDCS strategies for different boundary conditions. In this context, a novel hybrid optimization approach is presented, which combines dynamic programming with a genetic algorithm. Furthermore, a power to heat ratio is deduced, which is useful to interpret the results, and might support the design process of causal controllers considering the cabin heat demand.

Public perception of V2X-technology - evaluation of general advantages, disadvantages and reasons for data sharing with connected vehicles

Abstract: This work aims at an evaluation of vehicle-to-infrastructure (V2X)-technology through the users' perspective. The technical opportunities of connected vehicles are affected by the acceptance of the technology and possible draw-backs on the privacy and data-security side. With a three-tiered research approach, this work identified beforehand argument lines in focus group discussions, which enabled a quantitative approach to evaluate positively and negatively perceived features of V2X-technology. Also gender related differences can be displayed. Further, the results of the second quantitative study indicate that although users who already have experience with driver assistance systems are more willing to share (personal) data to use V2X-technology, the overall sample is very reserved with respect to sharing driver-related data. Future research on user diversity and cultural differences is outlined.

Decoupled cooperative trajectory optimization for connected highly automated vehicles at urban intersections

Abstract: The increasing market penetration of connected vehicles supports the development of highly automated vehicles for various traffic situations. Especially intersections form a bottleneck for the traffic flow and thus offer a high potential not only to increase the efficiency, but also to ensure safety. This paper presents a decoupled and decentralized approach using graph-based methods to optimize longitudinal trajectories for multiple vehicles at urban intersections. The approach enables the vehicles to cooperate, while avoiding collisions, considering dynamic influences like traffic lights, and minimizing a cost function. Furthermore, several heuristics are introduced, reducing the computational effort to solve these complex tasks. Simulations of an intersection scenario using the Monte Carlo method show a reduction of summarized costs, which represent travel time, efficiency and driving comfort, by ∼28% compared to a driver model and by ∼2.6% compared to a non-cooperative system.

Three Decades of Driver Assistance Systems: Review and Future Perspectives

Abstract: This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.

V2X Access Technologies: Regulation, Research, and Remaining Challenges

Abstract: As we edge closer to the broad implementation of intelligent transportation systems, the need to extend the perceptual bounds of sensor-equipped vehicles beyond the individual vehicle is more pressing than ever. Research and standardization efforts toward vehicle to everything (V2X), technology is intended to enable the communication of individual vehicles with both one another and supporting road infrastructure. The topic has drawn interest from a large number of stakeholders, from governmental authorities to automotive manufacturers and mobile network operators. With interest sourced from many disparate parties and a wealth of research on a large number of topics, trying to grasp the bigger picture of V2X development can be a daunting task. In this tutorial survey, to the best of our knowledge, we collate research across a number of topics in V2X, from historical developments to standardization activities and a high-level view of research in a number of important fields. In so doing, we hope to provide a useful reference for the state of V2X research and development for newcomers and veterans alike.

Fuel Economy Testing of Autonomous Vehicles

Abstract: Environmental pollution and energy use in the light-duty transportation sector are currently regulated through fuel economy and emissions standards, which typically assess quantity of pollutants emitted and volume of fuel used per distance driven. In the United States, fuel economy testing consists of a vehicle on a treadmill, while a trained driver follows a fixed drive cycle. By design, the current standardized fuel economy testing system neglects differences in how individuals drive their vehicles on the road. As autonomous vehicle (AV) technology is introduced, more aspects of driving are shifted into functions of decisions made by the vehicle, rather than the human driver. Yet the current fuel economy testing procedure does not have a mechanism to evaluate the impacts of AV technology on fuel economy ratings, and subsequent regulations such as Corporate Average Fuel Economy targets. This paper develops a method to incorporate the impacts of AV technology within the bounds of current fuel economy test, and simulates a range of automated following drive cycles to estimate changes in fuel economy. The results show that AV following algorithms designed without considering efficiency can degrade fuel economy by up to 3%, while efficiency-focused control strategies may equal or slightly exceed the existing EPA fuel economy test results, by up to 10%. This suggests the need for a new near-term approach in fuel economy testing to account for connected and autonomous vehicles. As AV technology improves and adoption increases in the future, a further reimagining of drive cycles and testing is required.