Online Trajectory Replanning for Sudden Environmental Changes During Automated Parking: A Parallel Stitching Method

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.

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Section: Targeted Nlp Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ouyang

et al. 2023

“…Local behavior planning and local trajectory planning functions work together to compute a safe, comfortable and continuous local trajectory based on the identified global route from route planning. Since the resultant trajectory is local, the two functions have to be implemented in a receding-horizon way unless the global destination is not far away [12]. It deserves to emphasize that the output of the two functions should be a trajectory rather than a path [13], [14], and the trajectory interacts with other dynamic traffic participants, otherwise, extra efforts are needed for the ego vehicle to evade the moving obstacles in the environment.…”

Section: B Local Behavior/trajectory Planningmentioning

confidence: 99%

Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving

Teng

Chen

et al. 2023

Thanks to the augmented convenience, safety advantages, and potential commercial value, Intelligent vehicles (IVs) have attracted wide attention throughout the world. Although a few autonomous driving unicorns assert that IVs will be commercially deployable by 2025, their implementation is still restricted to small-scale validation due to various issues, among which precise computation of control commands or trajectories by planning methods remains a prerequisite for IVs. This paper aims to review state-of-the-art planning methods, including pipeline planning and end-to-end planning methods. In terms of pipeline methods, a survey of selecting algorithms is provided along with a discussion of the expansion and optimization mechanisms, whereas in end-to-end methods, the training approaches and verification scenarios of driving tasks are points of concern. Experimental platforms are reviewed to facilitate readers in selecting suitable training and validation methods. Finally, the current challenges and future directions are discussed. The sideby-side comparison presented in this survey not only helps to gain insights into the strengths and limitations of the reviewed methods but also assists with system-level design choices.

“…Previous competitions typically focused on on-road driving scenarios [11]. However, on-road trajectory planners can hardly be applied to parking schemes owing to the following reasons [7], [12]. First, on-road planners do not support driving direction changes.…”

Section: Motivationsmentioning

confidence: 99%

Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives

Fan

Ouyang

et al. 2023

Self Cite

Automated parking is a typical function in a self-driving car. The trajectory planning module directly reflects the intelligence level of an automated parking system. Although many competitions have been launched for autonomous driving, most of them focused on on-road driving scenarios. However, driving on a structured road greatly differs from parking in an unstructured environment. In addition, previous competitions typically competed on the overall driving performance instead of the trajectory planning performance. A trajectory planning competition of automated parking (TPCAP) has been recently organized. This event competed on parking-oriented planners without involving other modules, such as localization, perception, or tracking control. This study reports the TPCAP benchmarks, achievements, experiences, and future perspectives.