The automation of the overtaking maneuver is considered to be one of the toughest challenges in the development of autonomous vehicles. This operation involves two vehicles (the overtaking and the overtaken) cooperatively driving, as well as the surveillance of any other vehicles that are involved in the maneuver. This operation consists of two lane changes-one from the right to the left lane of the road, and the other is to return to the right lane after passing. Lane-change maneuvers have been used to move into or out of a circulation lane or platoon; however, overtaking operations have not received much coverage in the literature. In this paper, we present an overtaking system for autonomous vehicles equipped with path-tracking and lane-change capabilities. The system uses fuzzy controllers that mimic human behavior and reactions during overtaking maneuvers. The system is based on the information that is supplied by a high-precision Global Positioning System and a wireless network environment. It is able to drive an automated vehicle and overtake a second vehicle that is driving in the same lane of the road.

There is a broad range of diverse technologies under the generic topic of intelligent transportation systems (ITS) that holds the answer to many of the transportation problems. In this paper, one approach to ITS is presented. One of the most important research topics in this field is adaptive cruise control (ACC). The main features of this kind of controller are the adaptation of the speed of the car to a predefined one and the keeping of a safe gap between the controlled car and the preceding vehicle on the road. We present an ACC controller based on fuzzy logic, which assists the speed and distance vehicle control, offering driving strategies and actuation over the throttle of a car. The driving information is supplied by the car tachometer and a RTK differential GPS, and the actuation over the car is made through an electronic interface that simulates the electrical signal of the accelerator pedal directly to the onboard computer. This control is embedded in an automatic driving system installed in two testbed mass-produced cars instrumented for testing the work of these controllers in a real environment. The results obtained in these experiments show a very good performance of the gap controller, which is adaptable to all the speeds and safe gap selections.

Research on adaptive cruise control (ACC) with Stop&Go maneuvers is presently one of the most important topics in the field of intelligent transportation systems. The main feature of such controllers is that there is adaptation to a user-preset speed and, if necessary, speed reduction to keep a safe distance from the vehicle ahead in the same lane of the road, whatever the speed. The extreme case is the stop and go operation in which the lead car stops and the vehicle at the rear must also do so. This paper presents the development of an ACC system and related experiments. The system input information is acquired by a real-time kinematic phase differential global positioning system (GPS) (i.e., centimetric GPS) and wireless local area network links. The outputs are the variables that control the pressure on the throttle and brake pedals, which is calculated by an onboard computer. In addition, the car control is based on fuzzy logic. The system has been installed in two mass-produced Citroe/spl uml/n Berlingo electric vans, in which all the actuators have been automated to achieve humanlike driving. The results from real experiments show that the unmanned vehicles behave very similarly to human-driven cars and are very adaptive to any kind of situation at a broad range of speeds, thus raising the safety of the driving and allowing cooperation with manually driven cars.

Vehicle and pedestrian collisions often result in fatality to the vulnerable road users, indicating a strong need of technologies to protect such vulnerable road users. Wireless communications have potential to support road safety by enabling road users to exchange information. In contrast to vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications for avoidance of inter-vehicle collisions, very limited efforts are made on communication mechanisms for pedestrian safety. This paper addresses the issue in a concrete way. We first formulate the requirement of the minimum information exchange distance for providing road users to have the necessary amount of time to perceive the situation and react. We then report our field tests and measurement based analysis to investigate if a Wi-Fi system can satisfy the application requirement. We also introduce a pedestrian protection application, V2ProVu, which provides the functionalities of the Wi-Fi communications, risk calculation, and hazard alarming. Our study discloses several useful insights including 1) information exchange for a velocity of 80 km/h has to be made before vehicle to pedestrian (V2P) distance is below 72 meters and 2) while this requirement is not too hard for radio communications technologies, the V2P communication range is greatly reduced if the signal is blocked by a human body.

SUMMARY: A system including Global Positioning Systems (GPS) and digital cartography is a good solution to carry out vehicle's guidance. However, it has inconveniences like high sensibility to multipath and interference when the GPS signal is blocked by external agents. Another system is mandatory to avoid this error. This paper presents a cooperative system based on GPS and Inertial Navigation Systems (INS) for automated vehicle position. The control system includes a decision unit to choose which value is the correct. In case GPS is working at top precision, it takes the control. On the other part, GPS signal can be lost and inertial control system guides the car in this occasion. A third possibility is contemplated: we receive the signal from GPS but the accuracy is over one meter. Now, position value is obtained by means of both systems. Experimental results analyze two situations: guidance in an urban area where GPS signal can be occluded by buildings or trees during short time intervals and the possibility of loss of the signal in long time to simulate the circulation in tunnels. Good results have been observed in tests and it demonstrates how a cooperative system improves the automated vehicle guidance.

Automated versions of a mass-produced vehicle use fuzzy logic techniques to both address common challenges and incorporate human procedural knowledge into the vehicle control algorithms. In-vehicle computing has been largely relegated to auxiliary tasks such as regulating cabin temperature, opening doors, and monitoring fuel, oil, and battery-charge levels. However, computers are increasingly assuming driving-related tasks in some commercial models. Among those tasks are: maintaining a reference velocity or keeping a safe distance from other vehicles; improving night vision with infrared cameras; and building maps and providing alternative routes