提出一种融合轨迹预测的决策规划方法，提升自动驾驶在复杂场景下的安全通行能力。
Maneuver Decision-Making with Trajectory Streams Prediction for Autonomous Vehicles
- 结合采样与搜索策略，迭代生成并优化不同驾驶动作的轨迹候选
- 在CommonRoad仿真中验证，可有效完成无保护左转、超车等动作
- 适合研究自动驾驶决策规划或高阶行为建模的开发者参考
决策、运动规划与轨迹预测是自动驾驶系统的关键环节。通过准确预测其他道路使用者的运动，可显著增强自主系统对动态和不可预测环境的响应能力，并适应多样化的道路场景。本文提出FFStreams++方法，用于实现无保护左转、超车和保持车道等多种驾驶动作的决策与运动规划。该方法结合基于采样的与基于搜索的策略：迭代生成不同动作的采样轨迹并进行优化，随后调用启发式搜索规划器寻找最优方案。将自动驾驶系统建模为规划领域定义语言（PDDL）问题，采用启发式快速前向规划器求解。通过轨迹流不断更新初始状态，生成特定动作的轨迹候选，逐步提升迭代层次直至找到最优计划。同时集成一个查询连接的网络模型，用于预测周围障碍物未来轨迹及其概率。所提方法在CommonRoad仿真框架上测试，采用随机生成的超车及交叉口无保护左转场景进行评估。实验结果表明，该方法能有效执行多种驾驶动作，确保安全性并降低与邻近交通参与者发生碰撞的风险。
原文摘要 · Abstract (English)
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.
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