A bilevel optimal motion planning (BOMP) model with application to autonomous parking

Shi, Shenglei; Xiong, Yan; Chen, Jiankui; Chen, Xiong

doi:10.1007/s41315-019-00109-z

Cited by 15 publications

(5 citation statements)

References 38 publications

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“…When a vehicle is travelling at a particular speed, the forward motion of a vehicle encounters an air force opposing its motion [8,9]. is force is called aerodynamic drag.…”

Section: Aerodynamic Dragmentioning

confidence: 99%

Retrospection of the Optimization Model for Designing the Power Train of a Formula Student Race Car

Kumar¹,

Jagota²,

Shabaz

2021

Scientific Programming

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This article describes the power train design specifics in Formula student race vehicles used in the famed SAE India championship. To facilitate the physical validation of the design of the power train system of a formula student race car category vehicle engine of 610 cc displacement bike engine (KTM 390 model), a detailed design has been proposed with an approach of easing manufacturing and assembly along with full-scale prototype manufacturing. Many procedures must be followed while selecting a power train, such as engine displacement, fuel type, cooling type, throttle actuation, and creating the gear system to obtain the needed power and torque under various loading situations. Keeping the rules in mind, a well-suited engine was selected for the race track and transmission train was selected which gives the maximum performance. Based on the requirement, a power train was designed with all considerations we need to follow. Aside from torque and power, we designed an air intake with fuel efficiency in mind. Wireless sensors and cloud computing were used to monitor transmission characteristics such as transmission temperature management and vibration. The current study describes the design of an air intake manifold with a maximum restrictor diameter of 20 mm.

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“…When a vehicle is travelling at a particular speed, the forward motion of a vehicle encounters an air force opposing its motion [8,9]. is force is called aerodynamic drag.…”

Section: Aerodynamic Dragmentioning

confidence: 99%

Retrospection of the Optimization Model for Designing the Power Train of a Formula Student Race Car

Kumar¹,

Jagota²,

Shabaz

2021

Scientific Programming

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“…The performance of the CCAR framework was validated using the NS‐2 environment with the SUMO traffic generator. To assess the effectiveness of the CCAR framework, it was compared against existing frameworks such as BOMP, 37 DPA, 38 RRT‐APF, 39 WFPP, 40 and IoT‐ILTMF 36 with respect to different measures such as energy consumption, throughput, end‐to‐end delay, routing overhead, and data loss ratio.…”

Section: Simulation Resultsmentioning

confidence: 99%

Cluster‐based context‐aware route service management for smart intelligent autonomous vehicles with industrial transport system

Nagappan,

Maheswari,

Siva

et al. 2023

Int J Communication

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SummaryIndustrial transport system refers to the movement of goods, raw materials, and finished products within and between industrial facilities such as factories, warehouses, and distribution centers. It includes a variety of modes of transport such as trucks, forklifts, conveyor belts, and automated guided vehicles (AGVs). Context‐aware route service management refers to system that can dynamically manage and optimize the routes taken by vehicles in transportation system based on contextual information. Our proposed framework for industrial transport systems and smart intelligent autonomous vehicles utilizes cluster‐based context‐aware routing (CCAR), which includes several significant contributions. Firstly, modified elephant herding optimization (MEHO) algorithm is used to efficiently group autonomous vehicles for routing. Secondly, an improved gravitational search (IGS) algorithm is used for cluster head (CH) selection responsible for transferring context information between vehicles (V2V). Thirdly, a deep hybrid multi‐graph neural network (DHMG‐NN) is designed for optimal neighboring roadside unit (RSU) node selection through different design constraints to ensure data dissemination between vehicles and infrastructure (V2I). Finally, we validate the effectiveness of our CCAR framework using various simulation scenarios. Our framework achieves a reduction of up to 78% in computation time and an improvement of up to 25% in customer satisfaction ratio and 15% higher than the overall performance ratio compared to the existing frameworks.

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“…the ones involving if-else conditions [21]. Some studies [30][31][32][33][34] converted conditional constraints and costs into differentiable forms. However, [30] or [31] assumes that a high-quality initial guess is available, which is not always true.…”

Section: Related Workmentioning

confidence: 99%

Mixed-Integer and Conditional Trajectory Planning for an Autonomous Mining Truck in Loading/Dumping Scenarios: A Global Optimization Approach

Ouyang

et al. 2023

IEEE Trans. Intell. Veh.

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Trajectory planning for a heavy-duty mining truck near the loading/dumping sites of an open-pit mine is difficult. As opposed to trajectory planning for a small-sized passenger car in a parking lot, trajectory planning for a heavy-duty mining truck involves complex factors in vehicle kinematics and environment. These factors make the concerned trajectory planning scheme a mixed-integer nonlinear program (MINLP) incorporated with conditional constraints (denoted as C-MINLP). MINLP solvers can neither deal with conditional constraints nor find global optima in real time. Instead of solving the C-MINLP directly, we build a from-coarse-to-fine framework so that the coupled difficulties (the mixed integral variables, conditional constraints, and the demand for global optimality) are divided and conquered. At the coarse search stage, a global-optimality-enhanced hybrid A* search algorithm is proposed to find a near-optimal coarse trajectory with the mixed integral variables, conditional kinematic constraints, and global optimality considered. The coarse trajectory is further polished at the refinement stage, wherein the nominal C-MINLP is simplified as a small-scale NLP. The solution to the NLP is an optimized trajectory, which does not violate the complex constraints in the nominal C-MINLP. This indicates that conversion from the C-MINLP to an NLP is efficient with the help of a high-quality coarse trajectory.

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A bilevel optimal motion planning (BOMP) model with application to autonomous parking

Cited by 15 publications

References 38 publications

Retrospection of the Optimization Model for Designing the Power Train of a Formula Student Race Car

Retrospection of the Optimization Model for Designing the Power Train of a Formula Student Race Car

Cluster‐based context‐aware route service management for smart intelligent autonomous vehicles with industrial transport system

Mixed-Integer and Conditional Trajectory Planning for an Autonomous Mining Truck in Loading/Dumping Scenarios: A Global Optimization Approach

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