Behavior Planning of Autonomous Cars with Social Perception
Behavior Planning of Autonomous Cars with Social Perception
复制标题
具有社会感知的自动驾驶汽车行为规划
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
M. Tomizuka
中科院分区：
文献类型：
--
作者：
Liting Sun;Wei Zhan;Ching;M. Tomizuka
自主汽车必须在充满不确定性的动态环境中导航。不确定性可能来自传感器限制，如遮挡和有限的传感器范围，或来自其他道路参与者的概率预测，或来自未知的社会行为在一个新的区域。为了在存在这些不确定性的情况下安全有效地驾驶，自动驾驶汽车的决策和规划模块应该智能地利用所有可用的信息，并适当地处理不确定性，以便生成适当的驾驶策略。在本文中，我们提出了一个社会感知计划，把所有的道路参与者在传感器网络中的分布式传感器。通过观察个体行为和群体行为，这三种类型的不确定性可以在一个信念空间中统一更新。更新的信念，从社会感知，然后明确纳入一个概率规划框架的基础上模型预测控制（MPC）。MPC的成本函数通过逆强化学习（IRL）来学习。这种具有社会增强感知的集成概率规划模块使自动驾驶车辆能够产生防御性但不过于保守且社会兼容的行为。仿真结果验证了所提出的框架的有效性与传感器闭塞的代表性方案。
Autonomous cars have to navigate in dynamic environment which can be full of uncertainties. The uncertainties can come either from sensor limitations such as occlusions and limited sensor range, or from probabilistic prediction of other road participants, or from unknown social behavior in a new area. To safely and efficiently drive in the presence of these uncertainties, the decision-making and planning modules of autonomous cars should intelligently utilize all available information and appropriately tackle the uncertainties so that proper driving strategies can be generated. In this paper, we propose a social perception scheme which treats all road participants as distributed sensors in a sensor network. By observing the individual behaviors as well as the group behaviors, uncertainties of the three types can be updated uniformly in a belief space. The updated beliefs from the social perception are then explicitly incorporated into a probabilistic planning framework based on Model Predictive Control (MPC). The cost function of the MPC is learned via inverse reinforcement learning (IRL). Such an integrated probabilistic planning module with socially enhanced perception enables the autonomous vehicles to generate behaviors which are defensive but not overly conservative, and socially compatible. The effectiveness of the proposed framework is verified in simulation on an representative scenario with sensor occlusions.
DOI：
10.1109/mits.2014.2306552
发表时间：
2014-06-01
影响因子：
3.6
作者：
Ziegler, Julius;Bender, Philipp;Zeeb, Eberhard
通讯作者：
Zeeb, Eberhard