Maneuver Decision-Making with Trajectory Streams Prediction for Autonomous Vehicles
<|reference_start|>Maneuver Decision-Making with Trajectory Streams Prediction for Autonomous Vehicles: Decision-making, motion planning, and trajectory prediction are crucial in autonomous driving systems. By accurately forecasting the movements of other road users, the decision-making capabilities of the autonomous system can be enhanced, making it more effective in responding to dynamic and unpredictable environments and more adaptive to diverse road scenarios. This paper presents the FFStreams++ approach for decision-making and motion planning of different maneuvers, including unprotected left turn, overtaking, and keep-lane. FFStreams++ is a combination of sampling-based and search-based approaches, where iteratively new sampled trajectories for different maneuvers are generated and optimized, and afterward, a heuristic search planner is called, searching for an optimal plan. We model the autonomous diving system in the Planning Domain Definition Language (PDDL) and search for the optimal plan using a heuristic Fast-Forward planner. In this approach, the initial state of the problem is modified iteratively through streams, which will generate maneuver-specific trajectory candidates, increasing the iterating level until an optimal plan is found. FFStreams++ integrates a query-connected network model for predicting possible future trajectories for each surrounding obstacle along with their probabilities. The proposed approach was tested on the CommonRoad simulation framework. We use a collection of randomly generated driving scenarios for overtaking and unprotected left turns at intersections to evaluate the FFStreams++ planner. The test results confirmed that the proposed approach can effectively execute various maneuvers to ensure safety and reduce the risk of collisions with nearby traffic agents.<|reference_end|>
@article{jamal2024maneuver,
    title={Maneuver Decision-Making with Trajectory Streams Prediction for
  Autonomous Vehicles},
    author={Mais Jamal and Aleksandr Panov},
    journal={arXiv preprint arXiv:2409.10165},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10165},
    primaryClass={cs.RO}
}