{'fraction_non_alphanumeric': 0.019832985386221295, 'fraction_numerical': 0.0010438413361169101, 'mean_word_length': 5.679442508710801, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Semantic-Level Maneuver Sampling and Trajectory Planning for On-Road Autonomous Driving in Dynamic Scenarios

Maneuver decision-making and trajectory planning play important roles in autonomous driving since a safe and flexible decision module is indispensable for navigation. Typical algorithms apply sampling methods to generate feasible trajectories. However, the fixed sampling distance and maneuver execution time in a sampling approach sacrifice the flexibility of algorithm. Moreover, since motion planning can be represented as a high-dimensional problem, it usually results in unnecessary samples that require additional resources to search the solution. Therefore, a semantic-level maneuver sampling and trajectory planning algorithm is proposed to solve the above problems. In the upper-level maneuver decision, the decision-making problem is formulated as a selection of the forward leading object. A semantic-level decision tree is built to sample long-term maneuver sequences, and the safety corridor of each maneuver sequence is calculated according to the surrounding environment. In the lower-level trajectory planning, the process is decoupled into longitudinal and lateral directions. First, a heuristic search method is proposed to generate longitudinal trajectory candidates for each maneuver sequence. Then, an exhaustive search algorithm is employed to synchronously generate the lateral trajectory within safety corridor. Among the generated trajectory candidates, the one with minimum cost will be chosen as the searching result. Furthermore, in order to improve the driving comfort, numerical optimization is adopted to refine the result by accounting for the constraints of kinematics and safety. Finally, the proposed method was evaluated through simulations of typical on-road dynamic scenarios, which help verify its performance with desirable computation efficiency of less than 32 ms.