A Collision Avoidance for Vehicle-Following Systems
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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 8, NO. 2, JUNE 2007
233Байду номын сангаас
Collision Avoidance for Vehicle-Following Systems
Stefan K. Gehrig and Fridtjof J. Stein
vehicle both longitudinally and laterally. This intuitive behavior comes to its limitations when other traffic participants interfere with the leader-vehicle’s path. Dynamic path modification becomes necessary. These dynamic driving situations motivated the work of this paper. How is this paper organized? Section II gives an overview of related work both in the fields of intelligent vehicles and robotics. In Section III, the elastic-band framework is introduced. Section IV describes the necessary adaptations to the elastic-band framework in order to tune the avoidance behavior toward that of human beings. In Section V, the extension of the elastic-band approach to nonholonomic vehicles is briefly discussed. The impact of the elastic-band framework on the planning and decision module is described in Section VI. Results are detailed in Section VII. Conclusions and future work comprise the final section. II. R ELATED W ORK A. Potential Field Approaches The most prominent idea for collision avoidance in robotics applications is artificial potential fields (e.g., [5] and [6]). The obstacles are modeled as potentials and the gradient of the superimposed potential field yields the direction command for the mobile robot. Potential fields are an elegant way to model obstacles and can be analyzed globally. A formal verification of the obstacle-avoidance behavior is analytically feasible. Generalized potential fields depend—not only—on distance to obstacles but can also depend, e.g., on obstacle velocity. Therefore, these fields can determine irrelevant obstacles moving away from the ego vehicle. This method was first applied using velocity-dependent potentials in [5]. A popular approach to avoid local minima, i.e., trapping situations, in potential field applications is the use of harmonic potentials (see, e.g., [7] and [8]). B. Approaches Using Physical Models Other physical models besides potential fields are also popular for collision avoidance. An alternative approach to the potential field method is presented in [9], where analogies of this problem to hydromechanics problems are shown. Fluid dynamics equations are used. The fluid starts at the starting point toward the goal point, and obstacles obstruct the flow. From the resulting flow field, the planned path can be computed. In [10], an approach to behavioral control of robots is described that uses the model of dynamical systems. Obstacles generate a stimulus that repels the autonomous robot. The path for navigation and obstacle avoidance is generated solving
I. I NTRODUCTION OLLISION avoidance has been the subject of extensive research both in the fields of robotics and intelligent vehicles. Since research on autonomous robots has been conducted for more than two decades, the lessons learned in that field also benefit the intelligent-vehicle research. A tremendous benefit is assessed for reducing collisions with automated systems in regular traffic [1]. The detection capabilities of vision-based intelligent vehicles are mature enough to perform such a task (see, e.g., [2]). For lateral vehicle guidance on highways, vision-based lane following is a promising and well-tested strategy (see, e.g., [3]). Recent work on lane recognition in urban areas is described in [4]. However, lane following relies on lane markings and tends to fail in cluttered scenes and in urban areas. Hence, a popular approach for automated vehicle guidance is to follow a leader
Abstract—The vehicle-following concept has been widely used in several intelligent-vehicle applications. Adaptive cruise control systems, platooning systems, and systems for stop-and-go traffic employ this concept: The ego vehicle follows a leader vehicle at a certain distance. The vehicle-following concept comes to its limitations when obstacles interfere with the path between the ego vehicle and the leader vehicle. We call such situations dynamic driving situations. This paper introduces a planning and decision component to generalize vehicle following to situations with nonautomated interfering vehicles in mixed traffic. As a demonstrator, we employ a car that is able to navigate autonomously through regular traffic that is longitudinally and laterally guided by actuators controlled by a computer. This paper focuses on and limits itself to lateral control for collision avoidance. Previously, this autonomous-driving capability was purely based on the vehicle-following concept using vision. The path of the leader vehicle was tracked. To extend this capability to dynamic driving situations, a dynamic path-planning component is introduced. Several driving situations are identified that necessitate responses to more than the leader vehicle. We borrow an idea from robotics to solve the problem. Treat the path of the leader vehicle as an elastic band that is subjected to repelling forces of obstacles in the surroundings. This elastic-band framework offers the necessary features to cover dynamic driving situations. Simulation results show the power of this approach. Real-world results obtained with our demonstrator validate the simulation results. Index Terms—Computer vision, intelligent vehicle, robotics, stereo vision.
233Байду номын сангаас
Collision Avoidance for Vehicle-Following Systems
Stefan K. Gehrig and Fridtjof J. Stein
vehicle both longitudinally and laterally. This intuitive behavior comes to its limitations when other traffic participants interfere with the leader-vehicle’s path. Dynamic path modification becomes necessary. These dynamic driving situations motivated the work of this paper. How is this paper organized? Section II gives an overview of related work both in the fields of intelligent vehicles and robotics. In Section III, the elastic-band framework is introduced. Section IV describes the necessary adaptations to the elastic-band framework in order to tune the avoidance behavior toward that of human beings. In Section V, the extension of the elastic-band approach to nonholonomic vehicles is briefly discussed. The impact of the elastic-band framework on the planning and decision module is described in Section VI. Results are detailed in Section VII. Conclusions and future work comprise the final section. II. R ELATED W ORK A. Potential Field Approaches The most prominent idea for collision avoidance in robotics applications is artificial potential fields (e.g., [5] and [6]). The obstacles are modeled as potentials and the gradient of the superimposed potential field yields the direction command for the mobile robot. Potential fields are an elegant way to model obstacles and can be analyzed globally. A formal verification of the obstacle-avoidance behavior is analytically feasible. Generalized potential fields depend—not only—on distance to obstacles but can also depend, e.g., on obstacle velocity. Therefore, these fields can determine irrelevant obstacles moving away from the ego vehicle. This method was first applied using velocity-dependent potentials in [5]. A popular approach to avoid local minima, i.e., trapping situations, in potential field applications is the use of harmonic potentials (see, e.g., [7] and [8]). B. Approaches Using Physical Models Other physical models besides potential fields are also popular for collision avoidance. An alternative approach to the potential field method is presented in [9], where analogies of this problem to hydromechanics problems are shown. Fluid dynamics equations are used. The fluid starts at the starting point toward the goal point, and obstacles obstruct the flow. From the resulting flow field, the planned path can be computed. In [10], an approach to behavioral control of robots is described that uses the model of dynamical systems. Obstacles generate a stimulus that repels the autonomous robot. The path for navigation and obstacle avoidance is generated solving
I. I NTRODUCTION OLLISION avoidance has been the subject of extensive research both in the fields of robotics and intelligent vehicles. Since research on autonomous robots has been conducted for more than two decades, the lessons learned in that field also benefit the intelligent-vehicle research. A tremendous benefit is assessed for reducing collisions with automated systems in regular traffic [1]. The detection capabilities of vision-based intelligent vehicles are mature enough to perform such a task (see, e.g., [2]). For lateral vehicle guidance on highways, vision-based lane following is a promising and well-tested strategy (see, e.g., [3]). Recent work on lane recognition in urban areas is described in [4]. However, lane following relies on lane markings and tends to fail in cluttered scenes and in urban areas. Hence, a popular approach for automated vehicle guidance is to follow a leader
Abstract—The vehicle-following concept has been widely used in several intelligent-vehicle applications. Adaptive cruise control systems, platooning systems, and systems for stop-and-go traffic employ this concept: The ego vehicle follows a leader vehicle at a certain distance. The vehicle-following concept comes to its limitations when obstacles interfere with the path between the ego vehicle and the leader vehicle. We call such situations dynamic driving situations. This paper introduces a planning and decision component to generalize vehicle following to situations with nonautomated interfering vehicles in mixed traffic. As a demonstrator, we employ a car that is able to navigate autonomously through regular traffic that is longitudinally and laterally guided by actuators controlled by a computer. This paper focuses on and limits itself to lateral control for collision avoidance. Previously, this autonomous-driving capability was purely based on the vehicle-following concept using vision. The path of the leader vehicle was tracked. To extend this capability to dynamic driving situations, a dynamic path-planning component is introduced. Several driving situations are identified that necessitate responses to more than the leader vehicle. We borrow an idea from robotics to solve the problem. Treat the path of the leader vehicle as an elastic band that is subjected to repelling forces of obstacles in the surroundings. This elastic-band framework offers the necessary features to cover dynamic driving situations. Simulation results show the power of this approach. Real-world results obtained with our demonstrator validate the simulation results. Index Terms—Computer vision, intelligent vehicle, robotics, stereo vision.