Abstract

This thesis deals with a model predictive control framework for control design of Advanced Driver Assistance Systems, where car-following tasks are under control. The framework is applied to design several autonomous and cooperative controllers and to examine the controller properties at the microscopic level and the resulting traffic flow characteristics at the macroscopic level. The results give new insights into impacts of ADAS on traffic flow characteristics.

Abstract

Vehicle-to-Vehicle communication has recently passed from a research topic to the subject of Field Operational Testing (FOT) and pilot deployment. Current state-of-the art Car-to-Car and Car-to-Infrastructure (C2X) functions will however only inform the driver, not interfere in actual vehicle operation. A logical next step after initial deployment will be sensor fusion to enhance actively intervening Advanced Driver Assistance Systems (ADAS) with information received over C2X. As the penetration rate of equipped vehicles increases over time, higher-level CoDAS functions become feasible. In this paper we present a concept for a cooperative active blind spot assistant (CABSA) as an exemplary function of these novel Cooperative Driver Assistance Systems. As the CABSA function improves an existing ADAS function, no negative effects are observed in low-penetration scenarios. The function was implemented with messages adhering European Telecommunications Standards Institute (ETSI) standards. Simulations and real-life tests show that the increase in operation range significantly expands the vehicle speed envelope upon which the system can prevent accidents compared to conventional blind-spot assistance.

Abstract

Modern automated and cooperative driver assistance systems (CoDAS) rely deeply on the position estimation. Regardless of absolute positioning accuracy, the relative position in regard to driving environment and other vehicles needs to be of high quality to enable sophisticated functions. Global Navigation Satellite Systems (GNSS) fulfill this demand only partially. In this paper we present an algorithm to accurately infer the driving lane by utilizing Dedicated Short Range Communication (DSRC) and map data alone. We evaluate our approach against simulated and real-life data from Europes largest cooperative vehicle Field Operational Test (FOT): simTD. This lane detection algorithm will be an enabler for CoDAS functions like collaborative driving and merging developed in the TEAM IP project.

Abstract

We exploit new types of vehicles, such as Plug-in Hybrid Electric Vehicles (PHEVs), to control transport related emissions in urban environments. By appropriately choosing whether single power-split hybrid vehicles should be operated in fully electric or in fuel mode we regulate where and how much pollution is delivered into the environment, without particular inconvenience to the vehicle owners. Preliminary ideas and simulation results are provided to illustrate the efficacy of the proposed approach.

Abstract

International audience; On the path to zero fatalities on the roadways, all vehicles have to periodically broadcast cooperative awareness messages (CAMs) in a timely and reliable manner, even in areas of high traffic density. The carrier senses multiple access with collision avoidance (CSMA/CA) scheme of IEEE 802.11, the de-facto standard for vehicular communications, is known to offer no reliability to broadcast packets that cannot be acknowledged, and to poorly perform at high network load due to collisions and interference. In this paper, an enhanced CSMA/CA protocol is analyzed for vehicular networks, which improves the CAM timeliness and reliability by leveraging full-duplex (FD) transceivers on board. FD devices can listen to the channel while transmitting, thus making collision detection viable. A FD vehicle can detect a CAM collision while sending, promptly abort the packet and retransmit it later. Results achieved through an analytical model under mathematically tractable assumptions, and through extensive system-level simulations in a complex urban environment, show the effectiveness of the protocol to cope with direct collisions, especially in high traffic areas, paving the way towards the realization of cooperative automated driving.