http://yetl.yabesh.ir/yetl1/handle/yetl/4274528 Sun, 03 May 2026 18:19:38 GMT 2026-05-03T18:19:38Z http://localhost:80/yetl1/bitstream/id/436501/ http://yetl.yabesh.ir/yetl1/handle/yetl/4274528 http://yetl.yabesh.ir/yetl1/handle/yetl/4310227 Reviewer’s Recognition The Editor-in-Chief and Editorial Board of the ASME Journal of Autonomous Vehicles and Systems would like to thank all the reviewers for volunteering their expertise and time reviewing manuscripts in 2024. Serving as reviewers for the journal is a critical service necessary to maintain the quality of our publication and to provide the authors with a valuable peer review of their work. Below is a complete list of reviewers for 2024. Wed, 01 Jan 2025 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4310227 2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4306575 Multifractal Terrain Generation for Evaluating Autonomous Off-Road Ground Vehicles Majhor, Casey D.; Bos, Jeremy P. We present a multifractal artificial terrain generation method that uses the 3D Weierstrass–Mandelbrot function to control roughness. By varying the fractal dimension used in terrain generation across three different values, we generate 60 unique off-road terrains. We use gradient maps to categorize the roughness of each terrain, consisting of low-, semi-, and high-roughness areas. To test how the fractal dimension affects the difficulty of vehicle traversals, we measure the success rates, vertical accelerations, pitch and roll rates, and traversal times of an autonomous ground vehicle traversing 20 randomized straight-line paths in each terrain. As we increase the fractal dimension from 2.3 to 2.45 and from 2.45 to 2.6, we find that the median area of low-roughness terrain decreases by 13.8% and 7.16%, the median area of semi-rough terrain increases by 11.7% and 5.63%, and the median area of high-roughness terrain increases by 1.54% and 3.33%, respectively. We find that the median success rate of the vehicle decreases by 22.5% and 25% as the fractal dimension increases from 2.3 to 2.45 and from 2.45 to 2.6, respectively. Successful traversal results show that the median root-mean-squared vertical accelerations, median root-mean-squared pitch and roll rates, and median traversal times all increase with the fractal dimension. Wed, 01 Jan 2025 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4306575 2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4306574 Robust Visual SLAM in Dynamic Environment Based on Motion Detection and Segmentation Yu, Xin; Shen, Rulin; Wu, Kang; Lin, Zhi In this study, we propose a robust and accurate simultaneous localization and mapping (SLAM) method for dynamic environments. Our approach combines sparse optical flow with epipolar geometric constraints to detect motion, determining whether a priori dynamic objects are moving. By integrating semantic segmentation with this motion detection, we can effectively remove dynamic keypoints, eliminating the influence of dynamic objects. This dynamic object removal technique is integrated into ORB-SLAM2, enhancing its robustness and accuracy for localization and mapping. Experimental results on the TUM dataset demonstrate that our proposed system significantly reduces pose estimation error compared to ORB-SLAM2. Specifically, the RMSE and standard deviation (S.D.) of ORB-SLAM2 are reduced by up to 97.78% and 97.91%, respectively, in highly dynamic sequences, markedly improving robustness in dynamic environments. Furthermore, compared to other similar SLAM methods, our method reduces RMSE and S.D. by up to 69.26% and 73.03%, respectively. Dense semantic maps generated by our method also closely align with the ground truth. Mon, 01 Jan 2024 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4306574 2024-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4306366 Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models Ren, Lichuan; Xi, Zhimin This paper addresses the critical need to quantify vehicle trajectory uncertainty in autonomous driving under environmental variability. We focus on predicting the posterior distribution of vehicle trajectories over a fixed horizon, given an initial state and a sequence of actions. We propose and compare three approaches: a probabilistic seq2seq model based on stochastic variational Gaussian processes, sequential Monte Carlo simulation with a single-step Gaussian process model, and a hybrid model that leverages the strengths of both methods. Each approach incorporates a baseline vehicle kinematics model to enhance stability and convergence. We evaluate these methods using a dataset generated from the CARLA simulator, assessing both point error metrics and probabilistic prediction metrics. This research introduces novel approaches to quantifying vehicle trajectory uncertainty through various uncertainty quantification techniques, with the goal of improving the safety and reliability of autonomous vehicle control systems. Mon, 01 Jan 2024 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4306366 2024-01-01T00:00:00Z