In this paper we present WILLEM, a system for dynamic evacuation routing in buildings, using a wireless sensor network. Dynamic evacuation routing is the process of dynamically determining the fastest routes to the exits. The routes may be changed in case a fire occurs somewhere. We also present an algorithm for detecting congestions in corridors during evacuation, and a means of providing the people in those congestions an alternative route towards the exit. Each phase of the method is descibed extensively: the deployment of the wireless sensor network, the automatic topology learning of the network and the actual evacuation routing methods. We have built a simulation framework in which all types of evacuation routing can be simulated. The results of our experiments were surprising in the sense that dynamic evacuation routing turned out not to be faster than static evacuation routing in every setup; however, we did find out why this is the case. We also performed some experiments on a real wireless sensor network, in order to find out if our automatic configuration method could work in real life. The results are promising. We also present an algorithm for mapping the learned topology of the wireless sensor network upon a virtual map. This way, the network topology can be visualised -which is an important feature for emergency services.

Abstract-This paper is concerned with safety in (cooperative) adaptive cruise control systems. In these systems, the speed of the cars is maintained automatically, based on the preferred speed of the driver and the speed of the preceding car. Technologies that are used in these systems, such as radar and radio communication, introduce many factors of uncertainty in the system. In this paper, we present models for different adaptive cruise control strategies, in which this uncertainty is explicitly modelled. By simulating emergency braking situations under these uncertain circumstances, we find the minimal safe time headway for these strategies.

The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective algorithm based on NEAT and SPEA2 that evolves controllers for such intelligent vehicles. The algorithm yields a set of solutions that embody their own prioritisation of various user requirements such as speed, comfort or fuel economy. This contrasts with most current research into such controllers, where the user preferences are summarised in a single number that the controller development process should optimise. Having multiple prioritisations of preferences would, however, allow the user to select desired vehicle behaviour in real time, for instance fast driving if she's in a hurry or economical driving in more relaxed circumstances. Preliminary results of our experiments show that evolved controllers substantially outperform the human behavioural model. We show that it is possible to evolve a set of vehicle controllers that correspond to different prioritisations of user preferences, giving the driver, on the road, the power to decide which preferences to emphasise.

Abstract-In the development of Cooperative Adaptive Cruise Control (CACC) systems, spacing policies are primarily developed for optimisation of string stability and traffic stability. However, the safety issue is hardly taken into account. Uncertainty in the communication network and sensor information makes deciding upon a safe minimal headway a non-trivial task. In this paper, we propose a model that is able to approximate the minimal safe time headway, given uncertainty of parameters with varying velocities. By simulating emergency stops, we use the difference in displacement of the cars and a desired maximum probability of a crash to approximate the minimum time headway that yields this probability of a crash. The resulting method is necessary for platooning, a major research development in vehicular networking systems.

Abstract. In this paper, we propose a technique for optimisation and online adaptation of search paths of unmanned aerial vehicles (UAVs) in search-and-identify missions. In these missions, a UAV has the objective to search for targets and to identify those. We extend earlier work that was restricted to offline generation of search paths by enabling the UAVs to adapt the search path online (i.e., at runtime). We let the UAV start with a pre-planned search path, generated by a Particle Swarm Optimiser, and adapt it at runtime based on expected value of information that can be acquired in the remainder of the mission. We show experimental results from 3 different types of UAV agents: two benchmark agents (one without any online adaptation that we call 'naive' and one with predefined online behaviour that we call 'exhaustive') and one with adaptive online behaviour, that we call 'adaptive'. Our results show that the adaptive UAV agent outperforms both the benchmarks, in terms of jointly optimising the search and identify objectives.Keywords: adaptive algorithm; design and engineering for self-adaptive systems; unmanned aerial vehicles; search and identify.