IFAC PapersOnLine 52-8 (2019) 176–181 ScienceDirect Available online at www.sciencedirect.com 2405-8963 © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2019.08.067 © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. 1. INTRODUCTION Highly Automated Driving (HAD) is getting more and more important to modern society and increases safety and comfort on the roads. ACC and Lane Keeping As- sistant (LKA) are only two exemplary modules in the wide field of advanced driver assistant systems. HAD is located one level below fully automated driving which relieves the driver from the driving activity and requires him to fulfill a monitoring passenger role only. The aim on one side is to decrease the number of accidents which are nowadays mostly caused by human failures, as reported in Singh (2015). On the other side, such systems offer more efficiency in the driving process and comfort for the driver. IAV GmbH has designed automated driving solutions for decades. During the last months, IAV GmbH has driven more than 300.000 km on highways in Germany, France, and the USA with its highly automated vehicle prototypes, which are also used for this study. An overview of different software architectures to realize HAD functionality can be found e.g in Ulbrich et al. (2015); Ta¸s et al. (2016). Recent approaches consist typi- cally of three layers – similar to classic robotic solutions, see Figure 1. The first layer realizes the environment perception with the main focus on object detection and prediction as well as on lane detection and free space Figure 1. Functional Architecture for HAD System calculation. The second layer consists of decision making, including strategic, tactical and operational planning. The strategy module realizes route calculation and abstract mission planning. The tactical module aims to generate optimal driving maneuver requests (e.g. lane change, lane keeping, parking) in order to follow the strategic route and actions. It continuously compares the driving state with the target state (e.g. target speed and target lane) and attempts to optimize the short term traveling behavior. The operational module supplies atomic driving maneu- vers such as lane keeping, lane change, etc. This lowest decision level knows the feasibility of each individual driv- ing maneuver and tries to fulfill the requirements of the tactical level by using path planning algorithms. Subse- quently, to the decision layer, the trajectory planning and lateral and longitudinal vehicle dynamic controllers realize detailed movement planning and tracking task inside the third layer. General longitudinal control approaches can be found e.g. in Rajamani (2012). Classical ACC controller architec- tures, including a fuzzy controller, are presented in Benalie et al. (2009); Ko and Lee (2007). These control structures were the starting point of the current approach. However, such simple approaches consider only one ACC object and as a result. With this kind of approaches, it is hard to match the increasing customer requirements regarding driver comfort in the case of crowded traffic conditions. As an improvement for this kind of approaches in the current study, a new multi-object based ACC solution is proposed and verified over real-time test drives. In this way, it becomes possible to cover the practical cases that can be faced in real-time traffic conditions. These practical cases and the contribution of the proposed approach is discussed within the scope of this study. More sophisticated approaches like model predictive con- trol based ACC are presented in Stanger and del Re (2013); Corona and De Schutter (2008). A reinforcement learning based ACC approach can be found in Desjardins and Chaib-draa (2011). Since the goal design of this study Keywords: Automated Guided Vehicles, Automobile Industry, Automotive Control, Dynamic Behaviour Abstract: Developments regarding driver assistant systems are one of the most active fields of research and development within the automotive industry. In the present paper, a longitudinal vehicle guidance concept for a multi-object Adaptive Cruise Control (ACC) will be introduced. The goal is to design a reliable, computationally efficient and intuitive approach for ACC which maximizes the drivers comfort in dense traffic conditions. Development of the control structure, as well as the target selection approach, are the central aspects of the paper. The results are verified with measurement data from test drives on the Boulevard P´eriph´erique in Paris. * Development Center Chemnitz/Stollberg, IAV GmbH, 09366 Stollberg, Germany (e-mail: {Frank.Schroedel, Norman01.Schwarz}@iav.de) Frank Schr¨odel * Patrick Herrmann * Norman Schwarz * An Improved Multi-Object Adaptive Cruise Control Approach