Philipp Themann

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

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 vehicles 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.

Discrete dynamic optimization in automated driving systems to improve energy efficiency in cooperative networks

Predictive and energy efficient driving styles considerably reduce fuel consumption and emissions of vehicles. Vehicle-to-vehicle and vehicle-to-infrastructure (V2X) communication provide information useful to further optimize fuel economy especially in urban conditions. This work summarizes an optimization approach integrating V2X information in the optimization of longitudinal dynamics. Besides the dimensions distance and velocity also the dimension time is reflected in discrete dynamic programming, which is based on a three-dimensional state space. Upcoming signal states of traffic signals are reflected in the optimization to implement an efficient pass through at intersections. Furthermore, simulated average driving behavior defines a reference for optimized velocity trajectories. This excludes optimization results strongly deviating from average behavior. The approach is implemented in a vehicle in a real-time capable way. In a field test the vehicle approaches a V2X traffic light and the optimization reduces fuel consumption by up to 15 % without increasing travel time.

Modular approach to energy efficient driver assistance incorporating driver acceptance

The deployment of predictive driving styles reduces fuel consumption of vehicles significantly, while assistance systems can support drivers in this task. This paper describes a modular approach to consider various sources of information as well as different driver and vehicle types in the prediction and the optimization of the vehicles longitudinal dynamics to reduce fuel consumption. Energy efficient driving strategies such as roll out or fuel cut-off are compared to the average driving behavior of the driver. The utility of the efficient strategies is assessed relative to the average driver behavior, which is similar to human information processing. Resulting optimal driving strategies are provided to the driver as recommendations or applied to vehicles by intervening assistance systems such as adaptive cruise control. This paper aims to summarize the basic methodology of the approach.

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

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.

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

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.

Definition of an embedded driver model for driving behavior prediction within the DESERVE platform

The use of driver models within advanced driver assistance systems (ADAS) allows anticipating the driving behavior of the vehicle and all traffic participants in the close vicinity. This valuable information could considerably improve the performance as well as the acceptance of ADAS. Consequently complex driver models need to be integrated in embedded systems. This work, first of all, aims to summarize important driver models described in literature. Based upon this a suitable approach to implement a driver model on an embedded system is derived. The model used, focuses on the longitudinal driving and lane change behavior of drivers. The system architecture is derived and optimized for real-time execution. The driver model is analyzed in detailed simulations. Test drives in a small scale naturalistic driving study are used to validate the driver model. This paper defines a standard driver model to be implemented as part of the DESERVE platform within the Artemis project “DESERVE”. As embedded automotive hardware the dSpace MicroAutoBox II is used. The paper summarizes approaches and examples to use the generated prediction data in ADAS like ACC.