Focused on the existing diagnosis methods of Parkinson's Disease （PD）， the auxiliary diagnosis methods of PD based on gait analysis was reviewed. In clinical practice， the common diagnosis method of gait assessment for PD is based on scales， which is simple and convenient， but is highly subjective and requires well-experienced clinical doctors. With the development of computer technology， more methods of gait analysis are provided. Firstly， PD and its abnormal manifestations in gait were summarized. Then， the common methods of auxiliary diagnosis for PD based on gait analysis were reviewed. These methods were able to be roughly divided into two types： methods based on wearable or non-wearable devices. Wearable devices are small and have high accuracy for diagnosis， and with the use of them， the gait status of patients can be monitored for a long time. With the use of non-wearable devices， human gait data is captured through video sensors such as Microsoft Kinect， without wearing related devices and restricting patients' movements. Finally， the deficiencies in the existing gait analysis methods were pointed out， and the possible development trends in the future were discussed.

In the automatic driving perception system， multi-sensor fusion is usually used to improve the reliability of the perception results. Aiming at the task of object detection in fusion perception system， a object detection method based on radar and camera fusion， namely Priori and Radar Region Proposal Network （PRRPN）， was proposed，with the aim of using radar measurement and the object detection result of the previous frame to improve the generation of region proposals in the image detection network and improve the object detection performance. Firstly， the objects detected in the previous frame with the radar points in the current frame were associated to pre-classify the radar points. Then， the pre-classified radar points were projected into the image， and the corresponding prior region proposals and radar region proposals were obtained according to the distance of the radar and Radar Cross Section （RCS） information. Finally， the regression and classification of the object bounding boxes were performed according to the region proposals. In addition， PRRPN and Region Proposal Network （RPN） were fused to carry out object detection. The newly released nuScenes dataset was adopted to test and evaluate the three detection methods. Experimental results show that， compared with RPN， the proposed PRRPN can not only detect objects faster， but also increase the average detection accuracy of small objects by 2.09 percentage points. And compared with the methods by using PRRPN and RPN alone， the method by fusing the proposed PRRPN and RPN has the average detection accuracy increased by 2.54 percentage points and 0.34 percentage points respectively.