The invention is applicable to the technical field of automatic driving assistance, and provides a lane keeping assistance method based on risk situation awareness constraint strategy optimization, which is characterized in that in the strategy reinforcement learning process, the risk of the lane keeping assistance is improved by implementing quantitative risk assessment and a safety constraint strategy optimization method oriented to risks of different levels. Therefore, the learned strategy can always meet the condition that the security constraint overhead is lower than the preset threshold value, and stable security control is realized. According to the method, the defect that the requirement for computing resources and time required by the MPC algorithm is high is overcome; meanwhile, the problem that a traditional deep reinforcement learning method is poor in safety when being used in the safety key field is solved. Test results show that the lane keeping assistance method based on risk situation awareness constraint strategy optimization can continuously and stably control the vehicle to stably and safely run in the expected lane, and high comfort is taken into account.
本发明适用于自动驾驶辅助技术领域,提供了一种基于风险态势感知约束策略优化的车道保持辅助方法,其在策略强化学习的过程中,通过实施量化风险评估,以及面向不同层级风险的安全约束策略优化方法,使得学习的策略能够始终满足安全约束开销低于预先设置的阈值,实现稳健的安全控制。本方法克服了MPC算法所需计算资源与时间要求较高的缺点;同时克服了传统的深度强化学习方法用于安全关键领域中安全性欠佳的问题。测试结果表明,基于风险态势感知约束策略优化的车道保持辅助方法能够持续稳定控制车辆在预期车道内平稳与安全行驶,并兼顾高舒适性。
Lane keeping assistance method based on risk situation awareness constraint strategy optimization
一种基于风险态势感知约束策略优化的车道保持辅助方法
2023-12-29
Patent
Electronic Resource
Chinese
IPC: | B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion |
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