Driving Space for Autonomous Vehicles

Yang, Diange; Jiao, Xinyu; Jiang, Kun; Cao, Zhong

doi:10.1007/s42154-019-00081-1

Cited by 10 publications

(4 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…to obtain information about their environment. When an obstacle is detected, an obstacle boundary is generated based on the external dimensions of the obstacle [30]. The various static and dynamic obstacles on the road then impose constraints on the trajectory planning of the ego-vehicle.…”

Section: Obstacle Avoidance Constraintmentioning

confidence: 99%

“…According to the discrete trajectory points, the expanded constraint function is brought into the corresponding cost function of Equation (30), and the constraint function is constructed as an interior point penalty function to obtain the CILQR algorithm. The CILQR algorithm solves the problem that the ILQR algorithm cannot deal with inequality constraints.…”

Section: Cilqr Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR

Wang,

Li,

Liu

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

With the development of science and technology, self-driving technology is gradually being applied to automobile semi-trailer trains. Aiming at the problem that it is challenging to plan the traveling trajectory of an autonomous semi-trailer train, a trajectory planning algorithm based on a constrained iterative linear quadratic regulator is proposed. The constrained iterative linear quadratic regulator solves the problem that the iterative constrained linear quadratic regulator cannot deal with inequality constraints by transforming inequality constraints into interior point penalty functions to deal with all kinds of inequality constraints in the trajectory planning problem. When dealing with the obstacle avoidance problem, the body contour is modeled approximately according to the actual shape of the vehicle, and the obstacle avoidance constraints are transformed into inequality constraints. Considering the unique body structure of a semi-trailer train, the articulation angle constraint function is designed according to different vehicle speeds. Simulation experimental results show that the algorithm can plan safe driving trajectories for semi-trailer trains in complex traffic scenarios.

show abstract

Section: Obstacle Avoidance Constraintmentioning

confidence: 99%

Section: Cilqr Algorithmmentioning

confidence: 99%

Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR

Wang,

Li,

Liu

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…(i) Drivable space classification. To assess the accuracy of drivable space classification, the output graph of SDS is transformed into a drivability grid, which is a common representation of drivable space [53]. The space is discretized into a uniform grid of resolution 0.2 meters [36], [37], and an average drivability for each cell is computed from intersecting edges (i.e.…”

Section: Sds Ground Truthmentioning

confidence: 99%

Situation-Aware Drivable Space Estimation for Automated Driving

Sánchez

Pogosov²,

Silvas

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

show abstract

“…Therefore, this paper focuses on EIS based on feature matching method. Image features include point feature, line feature, edge feature and so on [6]. Point feature has become a widely used feature description method because of its easy subsequent matching process.…”

Section: Introductionmentioning

confidence: 99%

Vehicular Electronic Image Stabilization System Based on a Gasoline Model Car Platform

Zhang

Yang

et al. 2022

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

Noise, vibration and harshness (NVH) problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles. Although high accuracy manufacturing, modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts; off-road condition, obstacles and extreme operating condition could still trigger NVH problems unexpectedly. This paper proposes a vehicular electronic image stabilization (EIS) system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles. Firstly, feature point detection and matching based on an oriented FAST and rotated BRIEF (ORB) algorithm are implemented to match images in the process of EIS. Furthermore, a novel improved random sampling consensus algorithm (i-RANSAC) is proposed to eliminate mismatched feature points and increase the matching accuracy significantly. And an adaptive Kalman filter (AKF) is applied to improve the adaptability of the vehicular EIS. Finally, an experimental platform based on a gasoline model car was established to validate its performance. The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.

show abstract

Driving Space for Autonomous Vehicles

Cited by 10 publications

References 88 publications

Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR

Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR

Situation-Aware Drivable Space Estimation for Automated Driving

Vehicular Electronic Image Stabilization System Based on a Gasoline Model Car Platform

Contact Info

Product

Resources

About