Autonomous Driving Trajectory Optimization With Dual-Loop Iterative Anchoring Path Smoothing and Piecewise-Jerk Speed Optimization

Zhou, Jinyun; He, Rong; Wang, Yu; Jiang, Shengyuan; Zhu, Zhiliang; Hu, Jiangtao; Miao, Jinghao; Luo, Qi

doi:10.1109/lra.2020.3045925

Cited by 43 publications

(15 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the difference between them is very small. Several papers have proposed the relationship between the jerk value and riding comfort [45][46][47]. Considering this result, CL50 was assumed to be the most comfortable.…”

Section: Scenario Of Experimentsmentioning

confidence: 99%

Experimental Study on Different Types of Curves for Ride Comfort in Automated Vehicles

Hashimoto

Takinami

Yamamoto³

2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

Vehicle automation is among the best possible solutions for traffic issues, including traffic accidents, traffic jams, and energy consumption. However, the user acceptance of automated vehicles is critical and is affected by riding comfort. In addition, human factors in automated vehicle control should be clear. This study evaluates the effect of different courses on driving comfort in automated vehicles using field experiments with 25 subjects. This study focused on lateral motion, but speed control was not targeted. Further, generating a path for obstacle avoidance and lane keeping, which have several constraining conditions, was also not targeted. Rendering a comfortable path is beneficial for developing an acceptable system as a car developer and for building new curves for automated or driving assistance systems from the perspective of construction. The automated vehicle drove at a speed of 30 km/h on four courses, namely, clothoid, two types of spline curves, and arc, based on the real intersection. Each participant sat on both the driver and passenger seat and answered a questionnaire. The experimental data indicated the clothoid course to be the most comfortable, while the arc was most uncomfortable for a significance level of 1%. These tendencies are applicable to driver and passenger seats, all genders, and experiences and will be beneficial for human factor research in automated vehicle control.

show abstract

Section: Scenario Of Experimentsmentioning

confidence: 99%

Experimental Study on Different Types of Curves for Ride Comfort in Automated Vehicles

Hashimoto

Takinami

Yamamoto³

2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Optimization problems widely exist in daily life and real-world engineering, such as resource allocation optimization [1], path planning optimization [2,3], and robot task allocation [4]. However, with the advent of Internet of Things and big data, optimization problems are becoming increasingly complicated [5,6], with many undesirable properties, such as non-differentiable, discontinuous, non-convex, non-linear and multimodal with many local areas [7].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, heuristic algorithms, like PSO, preserve unique merits in solving NP-hard problems [20,21]. (2) Heuristic algorithms usually preserve strong global search ability due to the maintenance of a population to search the solution space. Mathematical algorithms usually maintain only one solution to iteratively find the global optimum of an optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

et al. 2022

View full text Add to dashboard Cite

Optimization problems become increasingly complicated in the era of big data and Internet of Things, which significantly challenges the effectiveness and efficiency of existing optimization methods. To effectively solve this kind of problems, this paper puts forward a stochastic cognitive dominance leading particle swarm optimization algorithm (SCDLPSO). Specifically, for each particle, two personal cognitive best positions are first randomly selected from those of all particles. Then, only when the cognitive best position of the particle is dominated by at least one of the two selected ones, this particle is updated by cognitively learning from the better personal positions; otherwise, this particle is not updated and directly enters the next generation. With this stochastic cognitive dominance leading mechanism, it is expected that the learning diversity and the learning efficiency of particles in the proposed optimizer could be promoted, and thus the optimizer is expected to explore and exploit the solution space properly. At last, extensive experiments are conducted on a widely acknowledged benchmark problem set with different dimension sizes to evaluate the effectiveness of the proposed SCDLPSO. Experimental results demonstrate that the devised optimizer achieves highly competitive or even much better performance than several state-of-the-art PSO variants.

show abstract

“…A sampling-based method and an optimization-based method work better when they are combined [3,[10][11][12][13]. Concretely, a sampling-based method provides a resolutionfeasible coarse path/trajectory, which serves as the initial guess in solving an optimization problem via a local optimizer.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Parking Trajectory Planning With Tiny Passages: A Combination of Multistage Hybrid A-Star Algorithm and Numerical Optimal Control

Sheng

Zhong

2021

IEEE Access

View full text Add to dashboard Cite

This paper introduces an autonomous parking trajectory planning method in an unstructured environment with narrow passages. The proposed planner consists of two layers, namely a graph search layer and a numerical optimization layer. The contribution mainly lies in the graph search layer, wherein a multi-stage hybrid A* algorithm is proposed to handle narrow passages formed by obstacles in a tiny or cluttered environment. In the multi-stage hybrid A* algorithm, 2-dim A* algorithm is conducted to find a global route that connects the starting point and the goal point. Along the derived global route, subtle segments that traverse narrow passages will be extracted. Thereafter, the hybrid A* algorithm is used to plan kinematically feasible sub-paths that connect the boundary points of each subtle segment. The hybrid A* algorithm is also used to find the linking paths that connect adjacent sub-paths. Combing all of the subpaths and linking paths in a sequence yields a coarse path, which is converted into a coarse trajectory if attaching a time-optimal velocity profile to it. The coarse trajectory is fed into the numerical optimization layer as the initial guess. Simulation results indicate that our two-layer planner is much faster than the prevalent ones in dealing with unstructured environments with narrow passages.

show abstract

Autonomous Driving Trajectory Optimization With Dual-Loop Iterative Anchoring Path Smoothing and Piecewise-Jerk Speed Optimization

Cited by 43 publications

References 25 publications

Experimental Study on Different Types of Curves for Ride Comfort in Automated Vehicles

Experimental Study on Different Types of Curves for Ride Comfort in Automated Vehicles

Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

Autonomous Parking Trajectory Planning With Tiny Passages: A Combination of Multistage Hybrid A-Star Algorithm and Numerical Optimal Control

Contact Info

Product

Resources

About