Autonomous Pedestrian Collision Avoidance Using a Fuzzy Steering Controller

Abstract

Abstract-Collision avoidance is one of the most difficult and challenging automatic driving operations in the domain of intelligent vehicles. In emergency situations, human drivers are more likely to brake than to steer, although the optimal maneuver would, more frequently, be steering alone. This statement suggests the use of automatic steering as a promising solution to avoid accidents in the future. The objective of this paper is to provide a collision avoidance system (CAS) for autonomous vehicles, focusing on pedestrian collision avoidance. The detection component involves a stereo-vision-based pedestrian detection system that provides suitable measurements of the time to collision. The collision avoidance maneuver is performed using fuzzy controllers for the actuators that mimic human behavior and reactions, along with a high-precision Global Positioning System (GPS), which provides the information needed for the autonomous navigation. The proposed system is evaluated in two steps. First, drivers' behavior and sensor accuracy are studied in experiments carried out by manual driving. This study will be used to define the parameters of the second step, in which automatic pedestrian collision avoidance is carried out at speeds of up to 30 km/h. The performed field tests provided encouraging results and proved the viability of the proposed approach.

An Efficient Model-Free Setting for Longitudinal and Lateral Vehicle Control: Validation Through the Interconnected Pro-SiVIC/RTMaps Prototyping Platform

“…This method computes the yaw angle from the curvature (19), then the coordinates ψ d , x d and y d are deduced as follows by projection of the path length coordinate s d in the vehicle frame (s 0 being the initial condition): Fig. 3 shows an experimental validation of algorithm (20).…”

“…The vehicle longitudinal and lateral control problem has been widely investigated in the literature via model-based techniques (see, e.g., [2], [7], [8], [9], [10], [19], [20], [21], [27], [28], [30], [31], [23], [43], [33], [34], [36], [39], [41], Michel hugues.mounier@lss.supelec.fr and the references therein). Their performances are guaranteed in the vicinity of the model used for their implementation.…”

Coupled nonlinear vehicle control: Flatness-based setting with algebraic estimation techniques

“…Recently, a great many intelligent systems based controllers have been developed via lateral or longitudinal controllers. For lane keeping, lane-change maneuvers, pedestrian and obstacle avoidance, a lateral control is used (see, for example, Ackermann et al (1995); Guldner et al (1999); Tomizuka et al (1999); Rajamani et al (2000); Hatipoglu et al (2003); Zheng et al (2006); Plöchl & Edelmann (2007); Cerone et al (2009); Marino & Cinili (2009) ;Menhour et al (201); Fernandez-Llorca et al (2011)). While, for stop-and-go, adaptive cruise control and platooning tasks, the longitudinal control is developed (see for example Rajamani et al (2000); Mammar & Netto (2004); Martinez & Canudas-de-Wit (2007); Nouvelière & Mammar (2007); Villagra et al (2009)).…”

Autonomous Manoeuvring Systems for Collision Avoidance on Single Carriageway Roads

“…There is a large set of systems and research projects that uses the information supplied by these sensors and integrate it using obstacle detection and tracking algorithms. This way in [19,20] a stereovision system that detects pedestrians has been presented, including the necessary algorithms to detect and track a pedestrian, considering whether he is in the trajectory of the car and acting on the car to stop or deviate it if necessary. Similarly in [21,22] this data fusion has been complemented with GPS-based autonomous driving.…”

“…The application of artificial intelligence techniques for the automatic management of the actuators of the vehicle enables driver assistance systems and autonomous driving systems to perform management in a similar way to human drivers while improving safety and comfort [32]. Some obstacle identification-based collision avoidance applications which fall within the pre-collision systems group are the ones presented in [20,33] to avoid accidents with pedestrians, and the theoretical proposal in [34] for overtaking slow-moving vehicles.…”