Optimal Vehicle Path Planning Using Quadratic Optimization for Baidu Apollo Open Platform

Zhang, Yajia; Sun, Hongyi; Zhou, Jinyun; Pan, Jiacheng; Hu, Jiangtao; Miao, Jinghao

doi:10.1109/iv47402.2020.9304787

Cited by 53 publications

(17 citation statements)

References 13 publications

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“…As a pioneer in the autonomous driving industry in Mainland China, Baidu is jointly developing autonomous driving technology with automaker BMW and has set up test points in many regions, such as Shanghai and some cities in Anhui and Zhejiang. In the same year, Baidu launched a program, "Apollo", to provide new partners in the autonomous driving field with an open and secure framework to assist them in integrating vehicle and hardware systems to build customized autonomous driving systems rapidly [157]. Figure 10d shows an autonomous test vehicle from Baidu.…”

Section: Mainland Chinamentioning

confidence: 99%

Trends and Emerging Technologies for the Development of Electric Vehicles

Liu

Lau

et al. 2022

Energies

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In response to severe environmental and energy crises, the world is increasingly focusing on electric vehicles (EVs) and related emerging technologies. Emerging technologies for EVs have great potential to accelerate the development of smart and sustainable transportation and help build future smart cities. This paper reviews new trends and emerging EV technologies, including wireless charging, smart power distribution, vehicle-to-home (V2H) and vehicle-to-grid (V2G) systems, connected vehicles, and autonomous driving. The opportunities, challenges, and prospects for emerging EV technologies are systematically discussed. The successful commercialization development cases of emerging EV technologies worldwide are provided. This review serves as a reference and guide for future technological development and commercialization of EVs and offers perspectives and recommendations on future smart transportation.

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Section: Mainland Chinamentioning

confidence: 99%

Trends and Emerging Technologies for the Development of Electric Vehicles

Liu

Lau

et al. 2022

Energies

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“…However, non-holonomic constraints are not guaranteed to be satisfied. Zhang et al proposed a method which adopts several steps [19]. In the first step, in a Frenet frame, a smooth driving guide line is obtained, then, optimization is performed for path generation.…”

Section: A Related Workmentioning

confidence: 99%

A Hierarchical Motion Planning Framework for Autonomous Driving in Structured Highway Environments

Kim

et al. 2022

IEEE Access

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This paper presents an efficient hierarchical motion planning framework with a long planning horizon for autonomous driving in structured environments. A 3D motion planning with time information is a non-convex problem because there exists more than one local minimum point and various constraints such as roads and obstacles. Accordingly, to deal with high computational complexity and the problem of falling into a local minimum in an enormous solution space, a decoupled method is utilized, that consists of two steps: Long-term planning and short-term planning. First, the long-term planner provides reasonable far-sighted behavior through two processes. In the first process, a rough path that includes a driving strategy is generated in the 2D spatial space. Then, the jump point search algorithm is utilized with time information on the path to reduce the computational burden of A*, giving an acceptable quality of solution at the same time. In this step, a safe, comfortable, and dynamically feasible trajectory is generated. Next, the short-term planner optimizes a short-sighted trajectory using particle swarm optimization. In this method, a steering angle set is encoded as a particle, resulting in a safe, comfortable, and kinodynamically feasible trajectory. The proposed algorithm is implemented and evaluated in a range of vehicle-in-the-loop simulation scenarios, which include various virtual static and dynamic obstacles generated by Intelligent Driver Model. In the evaluation results, the proposed method reduced the computation time by up to 0.696 s with increasing the step cost by up to about 3%. The proposed algorithm is executed every 100 ms for a planning horizon of 10 seconds, and the average computation time is 31 ms with the worst-case computation time of 94 ms.

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“…The authors use an idea that combines dynamic programming (DP) and quadratic programming (QP). In another of their papers, they use a QP approach again to generate a system that is capable of avoiding collisions during motion planning [7].…”

Section: Related Workmentioning

confidence: 99%

Designing a Recurrent Neural Network to Learn a Motion Planner for High-Dimensional Inputs

Chiu¹

2022

Preprint

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The use of machine learning in the self-driving industry has boosted a number of recent advancements. In particular, the usage of large deep learning models in the perception and prediction stack have proved quite successful, but there still lacks significant literature on the use of machine learning in the planning stack. The current state of the art in the planning stack often relies on fast constrained optimization or rule-based approaches. Both of these techniques fail to address a significant number of fundamental problems that would allow the vehicle to operate more similarly to that of human drivers. In this paper, we attempt to design a basic deep learning system to approach this problem. Furthermore, the main underlying goal of this paper is to demonstrate the potential uses of machine learning in the planning stack for autonomous vehicles (AV) and provide a baseline work for ongoing and future research.

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Optimal Vehicle Path Planning Using Quadratic Optimization for Baidu Apollo Open Platform

Cited by 53 publications

References 13 publications

Trends and Emerging Technologies for the Development of Electric Vehicles

Trends and Emerging Technologies for the Development of Electric Vehicles

A Hierarchical Motion Planning Framework for Autonomous Driving in Structured Highway Environments

Designing a Recurrent Neural Network to Learn a Motion Planner for High-Dimensional Inputs

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