An improved RRT-based motion planner for autonomous vehicle in cluttered environments

Du, Mingbo; Chen, Jiajia; Zhao, Pan; Liang, Huawei; Yu, Xin; Mei, Tao

doi:10.1109/icra.2014.6907542

Cited by 32 publications

(21 citation statements)

References 15 publications

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“…It is well known that RRT-based planners often obtain meandering and jerky paths, which are bad for the vehicle to execute [ 8 , 19 , 36 ]. To solve this problem, a B-spline based post-processing method, which consists of two parts, namely, pruning part and smoothing part, is proposed for generating smooth paths.…”

Section: Dv-rrt Algorithmmentioning

confidence: 99%

“…Autonomous driving technology is an emerging field of research that attempts to accomplish its aim of driving safely and comfortably. This field has made considerable progress over the past few years [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. As the autonomous driving technology progresses, autonomous vehicles have become one of the most important matters supporting people’s daily life, the economy, and military activities.…”

Section: Introductionmentioning

confidence: 99%

“…However, the quality of grid-based algorithm is largely restricted to the grid resolution. In addition, since the memory usage grows exponentially with respect to the complexity of the problem or dimensionality of the state-space, so does the running time of the algorithm [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…The aforementioned RRT-based methods, however, continue to exhibit certain deficiencies in terms of motion planning of autonomous vehicle. RRT-based planners always generate jerky and unnatural trajectories that contain a number of unnecessary turns or unreachable positions [ 8 ]. These issues negatively affect the operations of autonomous vehicles because of the limited turning capability of the vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…RRT-based planners always generate jerky and unnatural trajectories that contain a number of unnecessary turns or unreachable positions [ 8 ]. These issues negatively affect the operations of autonomous vehicles because of the limited turning capability of the vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Drivers’ Visual Behavior-Guided RRT Motion Planner for Autonomous On-Road Driving

Mei

Liang

et al. 2016

Sensors

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This paper describes a real-time motion planner based on the drivers’ visual behavior-guided rapidly exploring random tree (RRT) approach, which is applicable to on-road driving of autonomous vehicles. The primary novelty is in the use of the guidance of drivers’ visual search behavior in the framework of RRT motion planner. RRT is an incremental sampling-based method that is widely used to solve the robotic motion planning problems. However, RRT is often unreliable in a number of practical applications such as autonomous vehicles used for on-road driving because of the unnatural trajectory, useless sampling, and slow exploration. To address these problems, we present an interesting RRT algorithm that introduces an effective guided sampling strategy based on the drivers’ visual search behavior on road and a continuous-curvature smooth method based on B-spline. The proposed algorithm is implemented on a real autonomous vehicle and verified against several different traffic scenarios. A large number of the experimental results demonstrate that our algorithm is feasible and efficient for on-road autonomous driving. Furthermore, the comparative test and statistical analyses illustrate that its excellent performance is superior to other previous algorithms.

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Section: Dv-rrt Algorithmmentioning

confidence: 99%