Abstract-Battery Electric Vehicles are becoming a promising technology for road transportation. However, the main disadvantage is the limited cruising range they can travel on a single battery charge. This paper presents a novel extended ecological cruise control system to increase the autonomy of an electric vehicle by using energy-efficient driving techniques. Driven velocity, acceleration profile, geometric and traffic characteristics of roads largely affect the energy consumption. An energy-efficient velocity profile should be derived based on anticipated optimal actions for future events by considering the electric vehicle dynamics, its energy consumption relations, traffic and road geometric information. A nonlinear model predictive control method with a fast numerical algorithm is adapted to determine proper velocity profile. In addition, a novel model to describe the energy consumption of a seriesproduction electric vehicle is introduced. The hyperfunctions concept is used to model traffic and road geometry data in a new way. The proposed system is simulated on a test track scenario and obtained results reveal that the extended ecological cruise control can significantly reduce the energy consumption of an electric vehicle.

Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an obstacle's space: a polyhedron, such as a cuboid, or a nonlinear differentiable surface, such as an ellipsoid. The former approach relies on disjunctive programming, which has a relatively high computational cost that grows exponentially with the number of obstacles. The latter approach needs to be linearized locally to find a tractable evaluation of the chance constraints, which dramatically reduces the remaining free space and leads to over-conservative trajectories or even unfeasibility. In this work, we present a hybrid approach that eludes the pitfalls of both strategies while maintaining the original safety guarantees. The key idea consists in obtaining a safe differentiable approximation for the disjunctive chance constraints bounding the obstacles. The resulting nonlinear optimization problem is free of chance constraint linearization and disjunctive programming, and therefore, it can be efficiently solved to meet fast real-time requirements with multiple obstacles. We validate our approach through mathematical proof, simulation and real experiments with an aerial robot using nonlinear model predictive control to avoid pedestrians.

Battery Electric Vehicle (BEV) has one of the most promising drivetrain technology. However, the BEVs are facing the limited cruising range which generally reduces their share in the automotive market. Velocity profile, acceleration characteristics, road gradients, and drive techniques around curves have significant impacts on the energy consumption of the BEVs. A semi-autonomous ecological driver assistance system to regulate the velocity with energy-efficient techniques is proposed to address the limitation. The main contribution of this paper is the design of a real-time nonlinear model predictive controller with improved inequality constraints handling and economic penalty function to plan the online cost-effective cruising velocity. This system is based on the extended cruise control driver assistance system which controls the longitudinal velocity of the BEV in a safe and energy efficient manner by taking advantage of road slopes, effective drive around curves, and respecting the traffic regulation. A realtime optimisation algorithm is adapted and extended with economic objective function. Instead of the conventional Euclidean norms, deadzone penalty functions are proposed to achieve the economic objectives. In addition, the states $ This work is supported by the FNR "Fonds national de la Recherche" (Luxembourg) through the AFR "Aidesà la Formation-Recherche" Ph.D. grant scheme No. 7041503.

Abstract-This paper assesses the impact of different spacing policies for Adaptive Cruise Control (ACC) systems on traffic and environment. The largest deal of existing studies focus on assessing the performance in terms of safety, while only few deal with the effect of ACC on the traffic flow and the environment. In particular, very little is know on traffic stability and energy consumption. In this study, the vehicles equipped with ACC are modelled and controlled by two different spacing policies. Besides, Human Driving Behavior (HDB) is modelled by using Gipps model for comparison and for simulating different penetration rates. As distinguished from other studies, vehicle dynamics and energy consumption of an electric car is formulated, which has completely different characteristics and limitations than combustion engine cars. Hence the study aims at providing additional understanding of how ACC-equipped electric vehicles will behave in dense traffic conditions. HDB and ACC vehicles are placed in a roundabout at different penetration rates. String stability and energy consumption are investigated by giving a shock wave to a stable traffic condition. It is found that ACC with quadratic spacing policy has significantly positive effects on string stability and energy consumption.

Autonomous route following with road vehicles has gained popularity in the last few decades. In order to provide highly automated driver assistance systems, different types and combinations of sensors have been presented in the literature. However, most of these approaches apply quite sophisticated and expensive sensors, and hence, the development of a cost-efficient solution still remains a challenging problem. This work proposes the use of a single monocular camera sensor for an automatic steering control, speed assistance for the driver and localization of the vehicle on a road. Herein, we assume that the vehicle is mainly traveling along a predefined path, such as in public transport. A computer vision approach is presented to detect a line painted on the road, which defines the path to follow. Visual markers with a special design painted on the road provide information to localize the vehicle and to assist in its speed control. Furthermore, a vision-based control system, which keeps the vehicle on the predefined path under inner-city speed constraints, is also presented. Real driving tests with a commercial car on a closed circuit finally prove the applicability of the derived approach. In these tests, the car reached a maximum speed of 48 km/h and successfully traveled a distance of 7 km without the intervention of a human driver and any interruption.