A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

Automatic and efficient driving strategies while approaching a traffic light

Vehicle-infrastructure communication opens up new ways to improve traffic flow efficiency at signalized intersections. In this study, we assume that equipped vehicles can obtain information about switching times of relevant traffic lights in advance. This information is used to improve traffic flow by the strategies 'early braking', 'anticipative start', and 'flying start'. The strategies can be implemented in driver-information mode, or in automatic mode by an Adaptive Cruise Controller (ACC). Quality criteria include cycle-averaged capacity, driving comfort, fuel consumption, travel time, and the number of stops. By means of simulation, we investigate the isolated strategies and the complex interactions between the strategies and between equipped and non-equipped vehicles. As universal approach to assess equipment level effects we propose relative performance indexes and found, at a maximum speed of 50 km/h, improvements of about 15% for the number of stops and about 4% for the other criteria. All figures double when increasing the maximum speed to 70 km/h.Comment: Submitted to ITSC - 17th International IEEE Conference on Intelligent Transportation System

The psychology of driving automation: A discussion with Professor Don Norman

Introducing automation into automobiles had inevitable consequences for the driver and driving. Systems that automate longitudinal and lateral vehicle control may reduce the workload of the driver. This raises questions of what the driver is able to do with this 'spare' attentional capacity. Research in our laboratory suggests that there is unlikely to be any spare capacity because the attentional resources are not 'fixed'. Rather, the resources are inextricably linked to task demand. This paper presents some of the arguments for considering the psychological aspects of the driver when designing automation into automobiles. The arguments are presented in a conversation format, based on discussions with Professor Don Norman. Extracts from relevant papers to support the arguments are presented

Behavioural compensation by drivers of a simulator when using a vision enhancement system

Technological progress is suggesting dramatic changes to the tasks of the driver, with the general aim of making driving environment safer. Before any of these technologies are implemented, empirical research is required to establish if these devices do, in fact, bring about the anticipated improvements. Initially, at least, simulated driving environments offer a means of conducting this research. The study reported here concentrates on the application of a vision enhancement (VE) system within the risk homeostasis paradigm. It was anticipated, in line with risk homeostasis theory, that drivers would compensate for the reduction in risk by increasing speed. The results support the hypothesis although, after a simulated failure of the VE system, drivers did reduce their speed due to reduced confidence in the reliability of the system