MomentaParking Perception｜Lidar感知算法

1. 负责自动驾驶视觉BEV/OCC 算法研发和工程落地 2. 探索使用新的深度学习方法来检测周围环境信息和交通参与者信息，提升实时感知模型的能力上限 3. 负责车端机器学习模型的性能评估/耗时优化/硬件友好部署 4. 通过模型优化和数据驱动等方式负责实车测试过程中问题的解决

Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. Now, NVIDIA’s GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world.We are now looking for an extraordinary Senior Perception Engineer to develop and productize NVIDIA’s autonomous driving solutions. As a member of our perception team, you will work on building world-class 3D obstacle perception solutions based on multi-sensor fusion, including cameras, ultrasonic sensors, and radar, to estimate high-resolution reconstruction of the world, such as occupancy networks. The primary approach will be deep learning. You will be challenged to improve robustness and accuracy as well as efficiency of the solutions to fully enable autonomous driving anywhere and anytime. What you’ll be doing: • Perception experts with application focus will be on multi-sensor fusion based deep learning model development for obstacle perception/fusion in complex driving environments. • Applied research and development of innovative deep learning and multi-sensor fusion algorithms to improve output accuracy of 3D obstacle perception solutions under challenging and diverse scenarios, with a focus on high-resolution world reconstruction (e.g., occupancy networks). • Identify and analyze the strength and weakness of the developed 3D obstacle perception solutions using large scale benchmark data (both real and synthetic) and improve them iteratively through KPI building and optimization. This includes careful data verification, model architecture design, understanding details of loss function engineering, and being capable of finding detailed ML bugs and iterating toward perfection. • Productize the developed 3D obstacle perception solutions by meeting product requirements for safety, latency, and SW robustness, with a strong emphasis on production deep learning model development. • Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high value data to improve perception system accuracy. Efforts will include data collection prioritization and planning, labeling prioritization, so that value of data is maximized.

•  Work on research, design and implementation of software features that are beneficial to our customers to meet their performance targets and build unique values•  Autonomous driving perception SW stack for improvement and issue fixes.•  Autonomous driving perception DNN improvement/eval/KPI for DNNs.•  Autonomous driving perception issue triage and root causing.•  Develop solutions for DNN model acceleration, optimization and deployment.

1. 负责ADAS、Parking、城区/高速NOA场景下的感知算法研发，包含车道线、停车位、斑马线、箭头等路面标识检测模型，以及交通灯、标识牌检测等模型开发工作。 2. 基于Transformer/GNN实现数据驱动的车端道路拓扑模型搭建； 3. 负责核心算法或模型的原创设计以及工程化落地，如模型优化、评测体系化构建、case迭代等 4. 掌握数据挖掘、标注、训练、部署及badcase回归等闭环链路，并伴随业务开展持续优化；掌握通过数据闭环持续迭代模型的能力；