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Proactive and Social navigation of autonomous vehicles in shared spaces

Abstract : The current trend in electric autonomous vehicles design is based on pre-existing models of cities which have been built for cars. The carbon footprint of cities cannot be reduced until the overall requirement for vehicles is reduced and more green and pedestrianized zones are created for better livability. However, such green zones cannot be scaled without providing autonomous mobility solutions, accessible to people with reduced mobility. Such solutions need to be capable of operating in spaces shared with pedestrians, which makes this a much harder problem to solve as compared to traditional autonomous driving. This thesis serves as a starting point to develop such autonomous mobility solutions. The work is focused on developing a navigation system for autonomous vehicles operating around pedestrians. The suggested solution is a proactive framework capable of anticipating pedestrian reactions and exploiting their cooperation to optimize the performance while ensuring pedestrians safety and comfort.A cooperation-based model for pedestrian behaviors around a vehicle is proposed. The model starts by evaluating the pedestrian tendency to cooperate with the vehicle by a time-varying factor. This factor is then used in combination with the space measurements to predict the future trajectory. The model is based on social rules and cognitive studies by using the concept of the social zones and then applying the deformable virtual zone concept (DVZ) to measure the resulting influence in each zone. Both parts of the model are learnt using a data-set of pedestrians to vehicle interactions by manually annotating the behaviors in the data-set.Moreover, the model is exploited in the navigation system to control both the velocity and the local steering of the vehicle. Firstly, the longitudinal velocity is proactively controlled. Two criteria are considered to control the longitudinal velocity. The first is a safety criterion using the minimum distance between an agent and the vehicle’s body. The second is proactive criterion using the cooperation measure of the surrounding agents. The latter is essential to exploit any cooperative behavior and avoid the freezing of the vehicle in dense scenarios. Finally, the optimal control is derived using the gradient of a cost function combining the two previous criteria. This is possible thanks to a suggested formulation of the cooperation model using a non-central chi distribution for the distance between the vehicle and an agent.A smooth steering is derived using a proactive dynamic channel method for the space exploration. The method depends on evaluating the navigation cost in a channel (sub-space) using a fuzzy cost model. The channel with the minimum cost is selected, and a human-like steering is affected using a Quintic spline candidate path between channels. Finally, the local steering is derived using a sliding mode path follower.The navigation is evaluated using PedSim simulator under ROS in pedestrian-vehicle interaction scenarios. The navigation is tested with different pedestrian density and sparsity. The proactive framework managed to navigate the vehicle producing smooth trajectories while maintaining the pedestrians’ safety and reducing the travel time in comparison with traditional reactive methods (Risk-RRT).
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Submitted on : Friday, May 13, 2022 - 2:31:32 PM
Last modification on : Wednesday, May 18, 2022 - 2:20:44 PM


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  • HAL Id : tel-03667564, version 2




Maria Kabtoul. Proactive and Social navigation of autonomous vehicles in shared spaces. Robotics [cs.RO]. Université Grenoble Alpes [2020-..], 2021. English. ⟨NNT : 2021GRALM059⟩. ⟨tel-03667564v2⟩



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