Collision avoidance support in roads with lateral and longitudinal maneuver prediction by fusing GPS/IMU and digital maps |
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| Institution: | 1. Technical University of Cartagena, Department of Electronics and Computer Technology, 30202 Cartagena, Spain;2. University of Murcia, Department of Information and Communications Engineering, Campus de Espinardo, 30001 Murcia, Spain;1. Service des maladies cardiovasculaires et thoraciques du CHU de Bouake, Bouaké, Côte d’Ivoire;2. Ong Wake Up Africa, Bouaké, Côte d’Ivoire;3. Département d’anthropologie et de sociologie, université Alassane Ouattara de Bouaké, Bouaké, Côte d’Ivoire;4. Entreprise à Bouake (Centre de la Côte d’Ivoire), Bouaké, Côte d’Ivoire;1. Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China;2. School of Electrical and Electronic Engineering, the University of Adelaide, Adelaide, SA 5005, Australia;1. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China;2. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China;1. Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, China;2. China Academy of Railway Sciences Signal & Communication Research Institute, Beijing 100081, China |
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| Abstract: | Collision avoidance in roads can be addressed in several ways, being cooperative systems one of the most promising options. In cooperative collision avoidance support systems (CCASS) the vehicles which constitute a scene share by means of communication links information that can be useful to detect a potentially risky situation. Typically, this information describes the kinematic state of each vehicle and can be complemented with a prediction of its next state. Indeed, the timely prediction of the next maneuver of a vehicle results beneficial to estimate the risk factor of a scene. This article presents a solution to the problem of maneuver prediction which employs a reduced number of sensors: a Global Navigation Satellite System (GNSS) receiver, one gyro, one accelerometer and the odometry. Predictions are made by a bi-dimensional interactive multiple model (2D-IMM) filter in which longitudinal and lateral motions of the vehicle are distinguished and maneuvering states are described by different kinematic models. A number of experiments were carried out with two vehicle prototypes in several circuits. The results achieved prove the suitability of the proposed method for the problem under consideration. |
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