An Overview of Motion-Planning Algorithms for Autonomous Ground Vehicles with Various Applications

Gautam, Ajitesh; He, Yuping; Lin, Xianke

doi:10.4271/10-08-02-0011

Cited by 3 publications

References 159 publications

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An Improved Vehicle Path-Tracking Model Based on Adaptive Nonlinear Model Predictive Control via Online Big Bang—Big Crunch Algorithm and Artificial Neural Network

Yangin,

Yalçın,

Akalin

2024

SAE Int. J. Veh. Dyn., Stab., and NVH

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<div>In this article, a novel tuning approach is proposed to obtain the best weights of the discrete-time adaptive nonlinear model predictive controller (AN-MPC) with consideration of improved path-following performance of a vehicle at different speeds in the NATO double lane change (DLC) maneuvers. The proposed approach combines artificial neural network (ANN) and Big Bang–Big Crunch (BB–BC) algorithm in two stages. Initially, ANN is used to tune all AN-MPC weights online. Vehicle speed, lateral position, and yaw angle outputs from many simulations, performed with different AN-MPC weights, are used to train the ANN structure. In addition, set-point signals are used as inputs to the ANN. Later, the BB–BC algorithm is implemented to enhance the path-tracking performance. ANN outputs are selected as the initial center of mass in the first iteration of the BB–BC algorithm. To prevent control signal fluctuations, control and prediction horizons are kept constant during the simulations. The results showed that all AN-MPC weights are successfully tuned online and updated during the maneuvers, and the path-following performance of the ego vehicle is improved at different NATO DLC speeds using the proposed ANN + BB–BC, compared to the method where ANN is used only.</div>

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An Improved Vehicle Path-Tracking Model Based on Adaptive Nonlinear Model Predictive Control via Online Big Bang—Big Crunch Algorithm and Artificial Neural Network

Yangin,

Yalçın,

Akalin

2024

SAE Int. J. Veh. Dyn., Stab., and NVH

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Trajectory Planning and Tracking Control for Single Lane Changing with Different Driving Styles of Intelligent Vehicles Based on Seventh-Degree Polynomial

Lai,

Huang

2024

SAE Int. J. CAV

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<div>Single lane changing is one of the typical scenarios in vehicle driving. Planning an appropriate lane change trajectory is crucial in autonomous and semi-autonomous vehicle research. Existing polynomial trajectory planning mostly uses cubic or quintic polynomials, neglecting the lateral jerk constraints during lane changes. This study uses seventh-degree polynomials for lane change trajectory planning by considering the vehicle lateral jerk constraints. Simulation results show that the utilization of the seventh-degree method results in a 41% reduction in jerk compared to the fifth-degree polynomial. Furthermore, this study also proposes lane change trajectory schemes that can cater to different driving styles (e.g., safety, efficiency, comfort, and balanced performance). Depending on the driving style, the planned lane change trajectory ensures that the vehicle achieves optimal performance in one or more aspects during the lane change process. For example, with the trajectory that provides the best comprehensive performance under given constraints (initial speed of 20 m/s, lane width of 3.5 m, and a longitudinal distance of 50 m to the obstacle in front), the four-wheel steering model predictive control can effectively track the planned trajectory, with the maximum jerk value being 6.4 m/s<sup>3</sup> and the longitudinal speed after lane change being approximately 12.6 m/s. Although this study assumes specific longitudinal displacement before and after the lane change, the methodology is applicable to other scenarios. For example, it can determine the shortest longitudinal displacement and the optimal lane change trajectory given predefined vehicle speeds and maximum lateral acceleration conditions. The lane change trajectories developed in this study can be directly applied to the system design of autonomous vehicles.</div>

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Driving Behavior Modeling Based on Inverse Reinforcement Learning

Xu,

Han,

Leng

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">With the advancement of intelligent driving technology, today’s smart vehicles must not only make accurate and safe driving decisions but also exhibit high human-likeness to ensure better acceptance from people. Developing vehicle behavior models with increased human-likeness has become a significant industry focus. However, existing vehicle behavior models often struggle to balance human-likeness and interpretability. While some researchers use inverse reinforcement learning (IRL) to model vehicle behavior, ensuring both human-likeness and a degree of interpretability, challenges such as reward function design difficulties and low human-likeness in background vehicle modeling persist. This study addresses these issues by focusing on highway scenarios without on-ramps, specifically following and lane-changing behaviors, using the CitySim dataset. IRL is employed to create a vehicle behavior model with improved human-likeness, utilizing a linear reward function to capture driver decision-making motives. Building on prior research, this study further explores various feature combinations for the reward function and introduces new features. The final feature combination resulted in a 12.6% and 14.4% reduction in planning errors on the training and test sets, respectively, compared to the baseline method. Additionally, the study enhances background vehicle modeling methods based on the Intelligent Driver Model (IDM) and the Minimizing Overall Braking Induced by Lane-change (MOBIL) model by adding traffic flow and patience correction terms. The results show that the improved background vehicle modeling method reduced test set errors by 4.3%, demonstrating greater human-likeness and making it more suitable for simulation environments.</div></div>

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An Overview of Motion-Planning Algorithms for Autonomous Ground Vehicles with Various Applications

Cited by 3 publications

References 159 publications

An Improved Vehicle Path-Tracking Model Based on Adaptive Nonlinear Model Predictive Control via Online Big Bang—Big Crunch Algorithm and Artificial Neural Network

An Improved Vehicle Path-Tracking Model Based on Adaptive Nonlinear Model Predictive Control via Online Big Bang—Big Crunch Algorithm and Artificial Neural Network

Trajectory Planning and Tracking Control for Single Lane Changing with Different Driving Styles of Intelligent Vehicles Based on Seventh-Degree Polynomial

Driving Behavior Modeling Based on Inverse Reinforcement Learning

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