Autonomous driving paper index
Parallel, Angular and Perpendicular Parking for Self-Driving Cars using Deep Reinforcement Learning
One-line summary
This paper presents an approach to the problem of autonomous parking using Reinforcement Learning, more precisely, Deep Deterministic Policy Gradient.
Engineering notes
Key topics: self-driving car, self-driving, reinforcement learning, control. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
The progress in creating a fully autonomous self-driving car has steadily increased in recent decades. Consequently, autonomous parking has been a well-researched field since every driving trip must end with a parking manoeuvre. In recent years, with the current successes in reinforcement learning, the concept of applying it to solve the autonomous parking problem has been more and more explored. A vehicle equipped with a complete autonomous parking system must perform three types of parking: perpendicular, angular and parallel parking. Autonomous parking systems control the steering angle and the vehicle speed by considering the surrounding space conditions to ensure collision-free motion within the available space. This paper presents an approach to the problem of autonomous parking using Reinforcement Learning, more precisely, Deep Deterministic Policy Gradient. This approach proved to be capable of parking in a variety of different environments for the three parking manoeuvres.
Links and sources
Need this topic turned into a technical roadmap?
Full Self Driving can prepare a custom autonomous driving literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments