Models and Performance of VANET based Emergency Braking

The network research community is working in the field of automotive to provide VANET based safety applications to reduce the number of accidents, deaths, injuries and loss of money. Several approaches are proposed and investigated in VANET literature, but in a completely network-oriented fashion. Most of them do not take into account application requirements and no one considers the dynamics of the vehicles. Moreover, message repropagation schemes are widely proposed without investigating their benefits and using very complicated approaches. This technical report, which is derived from the Master Thesis of Michele Segata, focuses on the Emergency Electronic Brake Lights (EEBL) safety application, meant to send warning messages in the case of an emergency brake, in particular performing a joint analysis of network requirements and provided application level benefits. The EEBL application is integrated within a Collaborative Adaptive Cruise Control (CACC) which uses network-provided information to automatically brake the car if the driver does not react to the warning. Moreover, an information aggregation scheme is proposed to analyze the benefits of repropagation together with the consequent increase of network load. This protocol is compared to a protocol without repropagation and to a rebroadcast protocol found in the literature (namely the weighted p-persistent rebroadcast). The scenario is a highway stretch in which a platoon of vehicles brake down to a complete stop. Simulations are performed using the NS_3 network simulation in which two mobility models have been embedded. The first one, which is called Intelligent Driver Model (IDM) emulates the behavior of a driver trying to reach a desired speed and braking when approaching vehicles in front. The second one (Minimizing Overall Braking Induced by Lane change (MOBIL)), instead, decides when a vehicle has to change lane in order to perform an overtake or optimize its path. The original simulator has been modified by - introducing real physical limits to naturally reproduce real crashes; - implementing a CACC; - implementing the driver reaction when a warning is received; - implementing different network protocols. The tests are performed in different situations, such as different number of lanes (one to five), different average speeds, different network protocols and different market penetration rates and they show that: - the adoption of this technology considerably decreases car accidents since the overall average maximum deceleration is reduced; - network load depends on application-level details, such as the implementation of the CACC; - VANET safety application can improve safety even with a partial market penetration rate; - message repropagation is important to reduce the risk of accidents when not all vehicles are equipped; - benefits are gained not only by equipped vehicles but also by unequipped ones

A Data Fusion Approach to Automated Decision Making in Intelligent Vehicles

The goal of an intelligent transportation system is to increase safety, convenience and efficiency in driving. Besides these obvious advantages, the integration of intelligent features and autonomous functionalities on vehicles will lead to major economic benefits from reduced fuel consumption to efficient exploitation of the road network. While giving this information to the driver can be useful, there is also the possibility of overloading the driver with too much information. Existing vehicles already have some mechanisms to take certain actions if the driver fails to act. Future vehicles will need more complex decision making modules which receive the raw data from all available sources, process this data and inform the driver about the existing or impending situations and suggest, or even take actions. Intelligent vehicles can take advantage of using different sources of data to provide more reliable and more accurate information about driving situations and build a safer driving environment. I have identified five general sources of data which is available for intelligent vehicles: the vehicle itself, cameras on the vehicle, communication between the vehicle and other vehicles, communications between vehicles and roadside units and the driver information. But facing this huge amount of data requires a decision making module to collect this data and provide the best reaction based on the situation. In this thesis, I present a data fusion approach for decision making in vehicles in which a decision making module collects data from the available sources of information and analyses this data and provides the driver with helpful information such as traffic congestion, emergency messages, etc. The proposed approach uses agents to collect the data and the agents cooperate using a black board method to provide the necessary data for the decision making system. The Decision making system benefits from this data and provides the intelligent vehicle applications with the best action(s) to be taken. Overall, the results show that using this data fusion approach for making decision in vehicles shows great potential for improving performance of vehicular systems by reducing travel time and wait time and providing more accurate information about the surrounding environment for vehicles. In addition, the safety of vehicles will increase since the vehicles will be informed about the hazard situations

Requirement analysis for building practical accident warning systems based on vehicular ad-hoc networks

An Accident Warning System (AWS) is a safety application that provides collision avoidance notifications for next generation vehicles whilst Vehicular Ad-hoc Networks (VANETs) provide the communication functionality to exchange these notifi- cations. Despite much previous research, there is little agreement on the requirements for accident warning systems. In order to build a practical warning system, it is important to ascertain the system requirements, information to be exchanged, and protocols needed for communication between vehicles. This paper presents a practical model of an accident warning system by stipulating the requirements in a realistic manner and thoroughly reviewing previous proposals with a view to identify gaps in this area