Conferences and supporting programme
Radar Sensors for Autonomous Driving: From Motion Measurement to 3-D Imaging
Due to accelerated research in the area of autonomous vehicle technology, it is inevitable that the cars will eventually drive themselves. In fact, quite a few self-driving cars have already logged thousands of hours and miles of tests. However, automated driving is challenging in the dense urban environment, due to higher traffic and obstruction densities, while automated highway driving requires navigation through drive-under conditions such as overhead bridges and tunnels. Current self-driving cars use a combination of several sensing technologies – radar, camera, ultrasonic and LIDAR. Higher costs for some of these technologies (e.g.: LIDAR) have hindered large-scale production. However, as technology innovations continue to reduce the costs of key sensing technologies such as radar and camera, it becomes easier for designers to bring self-driving cars into mainstream. Because radar sensors can work in every condition and even use reflections to see behind obstacles, they have found their way into wide range of ADAS applications. The radar sensors must be able to analyze a variety of complex scenarios, including both the higher traffic of urban environments and the drive-under conditions of highways, and extract all relevant information to take actions for safe maneuver. The sensors must provide better separation of objects and elevation estimation in these target conditions, translating to higher angular and velocity resolution requirements. This session discusses the most effective design of high performance MIMO radar using cascaded radar front-end devices to deliver less than 1-degree angular resolution, allowing for better separation of objects and elevator estimation. Reflections received by the high performance MIMO radar can be converted to 3-D images using advanced signal processing.
--- Date: 27.02.2018 Time: 4:00 PM - 4:30 PM Location: Conference Counter NCC Ost