A real-time motion planner with trajectory optimization for autonomous vehicles

Xu, Wenda; Wei, Junqing; Dolan, John M.; Zhao, Huijing; Zha, Hongbin

doi:10.1109/icra.2012.6225063

Cited by 95 publications

(26 citation statements)

References 13 publications

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“…Deterministic sample-based algorithms [10,23,24] explore the configuration space without applying any probabilistic function. For example, in graph-based approaches [23] both speed and space are completely discretized into a finite set of samples with a certain resolution.…”

Section: Path Planning Methodologiesmentioning

confidence: 99%

A Smart Many-Core Implementation of a Motion Planning Framework along a Reference Path for Autonomous Cars

et al. 2019

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Research on autonomous cars, early intensified in the 1990s, is becoming one of the main research paths in automotive industry. Recent works use Rapidly-exploring Random Trees to explore the state space along a given reference path, and to compute the minimum time collision-free path in real time. Those methods do not require good approximations of the reference path, they are able to cope with discontinuous routes, they are capable of navigating in realistic traffic scenarios, and they derive their power from an extensive computational effort directed to improve the quality of the trajectory from step to step. In this paper, we focus on re-engineering an existing state-of-the-art sequential algorithm to obtain a CUDA-based GPGPU (General Purpose Graphics Processing Units) implementation. To do that, we show how to partition the original algorithm among several working threads running on the GPU, how to propagate information among threads, and how to synchronize those threads. We also give detailed evidence on how to organize memory transfers between the CPU and the GPU (and among different CUDA kernels) such that planning times are optimized and the available memory is not exceeded while storing massive amounts of fuse data. To sum up, in our application the GPU is used for all main operations, the entire application is developed in the CUDA language, and specific attention is paid to concurrency, synchronization, and data communication. We run experiments on several real scenarios, comparing the GPU implementation with the CPU one in terms of the quality of the generated paths and in terms of computation (wall-clock) times. The results of our experiments show that embedded GPUs can be used as an enabler for real-time applications of computationally expensive planning approaches.

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Section: Path Planning Methodologiesmentioning

confidence: 99%

A Smart Many-Core Implementation of a Motion Planning Framework along a Reference Path for Autonomous Cars

et al. 2019

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“…This method sampling in state space only specifies a finite set of motion primitives, which reduces the motion potential of vehicles. To overcome these shortcomings, Xu et al [18] propose a two-step planning framework. Firstly, they sample separately in posture space (x, y, θ, κ) [24] and speed space, then search for a rough path and a rough speed profile, finally enter the optimization step to optimize the trajectory.…”

Section: A Related Workmentioning

confidence: 99%

“…Flexibility indicates the ability of an algorithm to adapt to different driving scenarios (i.e., following, parking and lane changes). Considering the computational complexity, some algorithms (i.e., [4], [18], [19], [35]) ignore the curvature constraints to improve the convergence speed of a trajectory. The state space term indicates whether the planning space is discrete or not.…”

Section: A Related Workmentioning

confidence: 99%

A Decoupled Trajectory Planning Framework Based on the Integration of Lattice Searching and Convex Optimization

Yang

et al. 2019

IEEE Access

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This paper presents a decoupled trajectory planning framework based on the integration of lattice searching and convex optimization for autonomous driving in structured environments. For a 3D trajectory planning problem with timestamps information, due to the presence of multiple kinds of constraints, the feasible domain is non-convex, so it is easy to fall into local optimum for trajectory planning. And the solution space of this problem is so enormous that it is difficult to identify an optimal solution in polynomial time. To address this non-convex problem, and to improve the convergence speed of an optimization process, the approach based on lattice searching is adopted in consideration of the ability to discretize driving environments and reduce the solution space. And the resulting path generated by lattice searching typically lies in the neighborhood of the global optimum. But this solution is neither spatiotemporally smooth nor globally optimal, so it is generally called the rough solution. For this reason, a subsequent nonlinear optimization process is introduced to refine the rough trajectory (combined by path and speed). The proposed framework is implemented and evaluated through simulations in various challenging scenarios in this paper. The simulation results verify that the trajectory planner can generate high-quality trajectories, and the execution time is also acceptable.

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“…Trajectory planning for unmanned ground vehicles is a well-studied problem and in the recent years, significant progress has been made in solving it using a wide variety of methods such as graph search, 6 stochastic tree search, 7,8 Markov decision processes (MDPs), and optimal control. 9,10 Graph search methods like state lattice search are popular global planning methods due to optimality guarantees and efficiency in large environments. Unmanned surface vehicles exhibit more motion uncertainty due to significant external disturbances like waves and winds.…”

Section: Introductionmentioning

confidence: 99%

Trajectory planning for unmanned surface vehicles operating under wave-induced motion uncertainty in dynamic environments

Rajendran

Moscicki

Wampler

et al. 2020

International Journal of Advanced Robotic Systems

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We present a deliberative trajectory planning method to avoid collisions with traffic vessels. It also plans traversal across wavefields generated by these vessels and minimizes the risk of failure. Our method searches over a state-space consisting of pose and time. And, it produces collision-free and minimum-risk trajectory. It uses a lookup table to account for motion uncertainty and failure risk. We also present speed-up techniques to increase performance. Our wave-aware planner produces plans that (1) have shorter execution times and safer when compared to previously developed reactive planning schemes and (2) comply with user-defined wave-traversal constraints and Collision Regulations (COLREGs)

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A real-time motion planner with trajectory optimization for autonomous vehicles

Cited by 95 publications

References 13 publications

A Smart Many-Core Implementation of a Motion Planning Framework along a Reference Path for Autonomous Cars

A Smart Many-Core Implementation of a Motion Planning Framework along a Reference Path for Autonomous Cars

A Decoupled Trajectory Planning Framework Based on the Integration of Lattice Searching and Convex Optimization

Trajectory planning for unmanned surface vehicles operating under wave-induced motion uncertainty in dynamic environments

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