Adaptive Dynamic Programming and Control for Autonomous Systems-Research Article An optimal trajectory planning algorithm for autonomous trucks: Architecture, algorithm, and experiment Feng Zhang 1 , Ranfei Xia 1 and Xinxing Chen 2 Abstract Safe lane changing of the dynamic industrial park and port scenarios with autonomous trucks involves the problem of planning an effective and smooth trajectory. To solve this problem, we propose a new trajectory planning method based on the Dijkstra algorithm, which combines the Dijkstra algorithm with a cost function model and the Bezier curve. The cost function model is established to filter target trajectories to obtain the optimal target trajectory. The third-order Bezier curve is employed to smooth the optimal target trajectory. Road experiments are carried out with an autonomous truck to illustrate the effectiveness and smoothness of the proposed method. Compared with other conventional methods, the improved method can generate a more effective and smoother trajectory in the truck lane change. Keywords Autonomous truck, trajectory planning, Dijkstra algorithm, cost functional model, Bezier curve Date received: 17 August 2019; accepted: 23 January 2020 Topic Area: Mobile Robots and Multi-Robot Systems Topic Editor: Lino Marques Associate Editor: Jian Huang Introduction Motivation In recent years, research on autonomous vehicles has entered a period of vigorous development. More and more autonomous vehicles are tested on real urban roads. Some companies even have begun the trial operation of autono- mous vehicles. In the passenger car field, Google Waymo has launched the self-driving taxi business in California, USA. In the commercial vehicle field, TuSimple has com- mercialized the road freight business in Arizona, USA. Autonomous vehicles use their sensing devices to perceive the surrounding environment, make executable safety deci- sions through the decision-making subsystem, confirm the effectiveness of the behavior, plan its trajectory, and finally complete autonomous driving. As a research branch of intelligent robots, many tech- nologies of autonomous vehicles are derived from intelli- gent robots. The trajectory planning of autonomous driving is equivalent to that of intelligent robots in a special scene. The trajectory planning of the intelligent robot is carried out in three-dimensional space, while the trajectory plan- ning of autonomous driving is similar, but limited to a two-dimensional plane. Therefore, the study of trajectory planning for autonomous driving can refer to current stud- ies on the trajectory planning of intelligent robots. 1–9 The 1 Department of Advanced Commodity Development, Dongfeng Commercial Vehicle Technical Center, Wuhan, China 2 Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China Corresponding author: Xinxing Chen, Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Room 115, South Building No. 1, Wuhan 430074, China. Email: cxx@hust.edu.cn International Journal of Advanced Robotic Systems March-April 2020: 1–11 ª The Author(s) 2020 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/1729881420909603 journals.sagepub.com/home/arx Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/ open-access-at-sage).