Toward human-like motion planning in urban environments

Gu, Tianyu; Dolan, John M.

doi:10.1109/ivs.2014.6856493

Cited by 49 publications

(22 citation statements)

References 19 publications

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“…Vehicle speed is always considered as a controlling factor that influences the points where actual steering angle changes. 8 This research focuses on the varying lateral character-steering wheel angle on the curves. To reduce the impact of speed on steering angles, participants were required to drive through the curves at specified speeds which were 20, 30, 40, and 50 km/h.…”

Section: Experimental Processmentioning

confidence: 99%

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Human-like trapezoidal steering angle model on two-lane urban curves

Jiang

Zhou

et al. 2019

International Journal of Advanced Robotic Systems

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With the rapid development of automated vehicles, there is currently a significant amount of automated driving research. Giving automated vehicles capabilities similar to those of experienced drivers will allow them to share the road harmoniously with manned vehicles, especially on two-lane urban curves. To represent the steering behavior of experienced drivers, a series of curve feature distances are proposed, which is determined by multi-regression. These series of curve feature distances are used to generate a trapezoidal steering angle model which imitates the steering behavior of the experienced test drivers. To verify the feasibility and human-likeness of the proposed trapezoidal steering angle model, the model is used with constant vehicle speed to plan a human-like trajectory which is tracked using model predictive control. The simulation results show that the proposed trapezoidal steering angle model is human-like and could be used to give automated vehicles human-like driving capability when driving on two-lane curves.

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Section: Experimental Processmentioning

confidence: 99%

“…7 The geographical features of roads are considered to achieve human-like speed. 8,9 As mentioned in the first classification, CNN is a method to imitate the trajectory of human driver. 10 However, the trajectory obtained through CNN is only suitable for the fixed traffic environment.…”

Section: Introductionmentioning

confidence: 99%

Human-like trapezoidal steering angle model on two-lane urban curves

Jiang

Zhou

et al. 2019

International Journal of Advanced Robotic Systems

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“…The vehicle motion in different road scenarios is also governed by the road geometry, i.e., curvy roads force the driver to slow down the vehicle to reduce the driving discomfort caused by increasing lateral accelerations. Gu and Dolan [21], proposed a geometry based speed planning approach in which a reference path was generated for the autonomous vehicle by combining the smooth and peak-value-reduced curvature and a parameterized speed model that was fitted from human driving data. While this was a step towards achieving the human-like naturalistic driving behaviours, the reference speed model used in their work was solely built on the geometric nature of the reference path and ignored other factors such as the motion of other actors and their interaction.…”

Section: B Related Workmentioning

confidence: 99%

Autonomous Navigation in Interaction-Based Environments—A Case of Non-Signalized Roundabouts

Rodrigues

McGordon

Gest

et al. 2018

IEEE Trans. Intell. Veh.

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To reduce the number of collision fatalities at crossroads intersections, many countries have started replacing intersections with non-signalized roundabouts, forcing the drivers to be more situationally aware and to adapt their behaviors according to the scenario. A non-signalized roundabout adds to the autonomous vehicle planning challenge, as navigating such interaction-dependent scenarios safely, efficiently, and comfortably has been a challenge even for human drivers. Unlike traffic signal-controlled roundabouts, where the merging order is centrally controlled, driving a non-signalized roundabout requires the individual actor to make the decision to merge based on the movement of other interacting actors. Most traditional autonomous planning approaches use rule-based speed assignment for generating admissible motion trajectories, which work successfully in non-interaction-based driving scenarios. They, however, are less effective in interaction-based scenarios as they lack the necessary ability to adapt the vehicle's motion according to the evolving driving scenario. In this paper, we demonstrate an adaptive tactical behavior planner (ATBP) for an autonomous vehicle that is capable of planning human-like motion behaviors for navigating a nonsignalized roundabout, combining naturalistic behavior planning and tactical decision-making algorithm. The human driving simulator experiment used to learn the behavior planning approach and the ATBP design is described in this paper. Index Terms-Adaptive tactical behaviour planner, adaptive control, human factors, naturalistic driving, trajectory planning. I. INTRODUCTION C URRENTLY autonomous or self-driving vehicles are at the heart of academia and industry research because of their multi-faceted advantages that include improved safety, reduced congestion, lower emissions, greater mobility etc. Significant advancement in digital technology (sensing, processing etc.) has pushed the autonomous technology in ground vehicle Manuscript

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“…Years of research have borne fruits to many motion planning frameworks [2]- [7]. The literature can be roughly divided into three categories of approaches: sampling based approaches, Model Predictive Control (MPC) based approaches and path-velocity decomposition approaches.…”

Section: B Related Workmentioning

confidence: 99%

“…The path planning problem determines a kinematically feasible (curvature-continuous) path along the road. Various path generation methods are proposed using cubic curvature polynomials [11], Bézier curves [5], [12], clothoid tentacles [13], Dubin's paths [14], and nonlinear optimization technique [7]. Quintic Bézier curves [5] is a promising approach because it is easy to compute and tune.…”

Section: B Related Workmentioning

confidence: 99%

Motion planning for urban autonomous driving using Bézier curves and MPC

Qian

Navarro

Fortelle

et al. 2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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This paper presents a real-time motion planning scheme for urban autonomous driving that will be deployed as a basis for cooperative maneuvers defined in the European project AutoNet2030. We use a path-velocity decomposition approach to separate the motion planning problem into a path planning problem and a velocity planning problem. The path planner first generates a collision-free piecewise linear path and then uses quintic Bézier curves to smooth the path with C 2 continuity. A derive-free optimization technique Subplex is used to further smooth the curvature of the path in a besteffort basis. The velocity planner generates an optimal velocity profile along the reference path using Model Predictive Control (MPC), taking into account user preferences and cooperative maneuver requirements. Simulation results are presented to validate the approach, with special focus on the flexibility, cooperative-awareness and efficiency of the algorithms.

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Toward human-like motion planning in urban environments

Cited by 49 publications

References 19 publications

Human-like trapezoidal steering angle model on two-lane urban curves

Human-like trapezoidal steering angle model on two-lane urban curves

Autonomous Navigation in Interaction-Based Environments—A Case of Non-Signalized Roundabouts

Motion planning for urban autonomous driving using Bézier curves and MPC

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