Navigation-based constrained trajectory generation for advanced driver assistance systems

Daniel, Jérémie; Birouche, Abderazik; Lauffenburger, Jean-Philippe; Basset, Michel

doi:10.1504/ijvas.2011.041389

Cited by 18 publications

(23 citation statements)

References 18 publications

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“…A vehicle is traveling along a predefined road (here we do not deal with the navigation in a road network) and it is able to use the available lateral space on the street to minimize a criteria like the path curvature. and orientation defines the valid area of the road (graphic is based on [8]). Furthermore, we introduce an additional constraint to enable real-time path replanning capabilities that incorporate the actual vehicle state.…”

Section: Real-time Capable Path Planning For Energy Management Systemmentioning

confidence: 99%

“…As a preliminary work we examined two promising approaches of the publications [8] and [9]. The first approach in [8] is based on the idea that every road boundary as well as the vehicle path itself can be represented as two splines in the direction of and ( Fig. 2):…”

Section: A State Of the Art In Vehicle Path Planningmentioning

confidence: 99%

“…also between the interpolation points. Our problem formulation uses polynomials as the path representation (like in [8]), but with interpolation points fixed on a line between the boundary knots like in [9]. The benefit of this representation is that we are able to reduce the eight optimization variables (compare eq.…”

Section: B the New Combined Approachmentioning

confidence: 99%

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Real-time capable path planning for energy management systems in future vehicle architectures

Brembeck¹,

Winter

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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In this paper an energy optimal path planning and velocity profile generation for our highly maneuverable Robotic Electric Vehicle research platform ROboMObil is presented. The ROMO [1] is a development of the German Aerospace Center's Robotics and Mechatronics Center to cope with several research topics, like energy efficient, autonomous or remote controlled driving for future (electro-) mobility applications. The main task of the proposed algorithms is to calculate an energy optimal trajectory in a real-time capable way. It is designed to incorporate data from actual traffic situations (e.g. oncoming traffic) or changed conditions (e.g. snowy conditions). The resulting trajectory is then fed forward to a lower level time independent path following control [2] that calculates the motion demands for our energy optimal control allocation. This in turn distributes the demand to the actuators of the over-actuated vehicle. We show a numerical reliable way to formulate the energy optimal path planning optimization objective, which is able to provide a consistent replanning feature considering the actual vehicle states. Besides this, different types of optimization methods are evaluated for their real-time capabilities. The velocity profile will be calculated afterwards and the generation of the profile is also enabled to handle dynamic replanning. Finally, we show several experimental results, using a virtual road definition and tests on a commercial real-time platform.

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Section: Real-time Capable Path Planning For Energy Management Systemmentioning

confidence: 99%

Section: A State Of the Art In Vehicle Path Planningmentioning

confidence: 99%

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Real-time capable path planning for energy management systems in future vehicle architectures

Brembeck¹,

Winter

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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“…This led to the situation that -despite Kladek having introduced the equation to describe urban vehicular flow -today the Kladek formula is and has been applied predominantly for pedestrian dynamics and engineering [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] with only few literature search results related to vehicular traffic [29]. One might stumble here over the fact that Kladek's system -urban road traffic -is essentially one-dimensional, while Weidmann used the very same function to describe a 2d system, as Weidmann does not at all limit the applicability to single-file movement.…”

Section: Introduction Ii: the Kladek Formulamentioning

confidence: 99%

The Inflection Point of the Speed-Density Relation and the Social Force Model

2016

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It has been argued that the speed-density diagram of pedestrian movement has an inflection point [1] (p. 3, "Domain I: ... At low densities there is a small and increasing decline of the velocity ... Domain III: ... For growing density the velocity remains nearly constant."). This inflection point was found empirically in investigations of closed-loop single-file pedestrian movement.The reduced complexity of single-file movement does not only allow a higher precision for the evaluation of empirical data, but it also significantly simplifies analytical considerations. This is especially true if one assumes homogeneous conditions, i.e. neglects temporal variations (consider time averages, neglect stop-and-go waves), individual differences of pedestrians (all simulated pedestrians have identical parameters) and investigates only steady-state (not the initial phase). As will be shown in this contribution one then can make a transition from the microscopic to a continuous and macroscopic perspective.Building on that it will be shown that certain (common) variants of the Social Force Model (SFM) do not produce an inflection point in the speed-density diagram if -assuming periodic boundary conditions -infinitely many pedestrians contribute to the force computed for one pedestrian. It will furthermore be shown that if -in said 1d movement situation -one only considers nearest neighbors for the computation of the interpedestrian forces the Social Force Model in the continuous description results in the so called Kladek formula for the speed-density relation. Since the Kladek formula exhibits the desired inflection point this observation is used as a motivation for an extension of the Social Force Model which allows to transform the continuous description of the SFM continuously to the Kladek formula and which also exhibits the inflection point in the speed density relation. It will be shown then, that this extended SFM yields astonishingly similar speed density relations as the original SFM when only a fixed limited number of (nearest) pedestrians are considered in the computation of the inter-pedestrian force. Finally it will be discussed, if also the description of the speed-density diagram for (motorized, four-wheel) vehicular and/or bicycle traffic could benefit from these measures.

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“…A particular optimal control path planning algorithm is therefore generally tailored to a certain traffic situation or maneuver (e.g. Attia et al, 2012;Daniel et al, 2011).…”

Section: Introductionmentioning

confidence: 99%