In-vehicle people sensing has received considerable attention for preventing overloading and forgotten children. Impulse radio ultrawideband (IR-UWB) radar is widely applied in noncontact sensing applications. However, metal-rich environment and narrow space with multiple objects bring dense multipaths in received radar signals, posing a great challenge to in-vehicle sensing, especially people counting and motion recognition. This article proposes novel spatial-temporal-circulated gray level co-occurrence matrix (STC-GLCM) and physiological features for people sensing, to detect the presence, number, and motions of people in the vehicle. Considering temporal consistency and spatial continuity of highly associated in-vehicle radar signals, STC-GLCM feature is designed to describe the 2-D circular spatial-temporal distribution, and extracted at individual, associated, and global scales. In addition, the singular value ratio (SVR) is introduced to select the physiological feature at each individual scale, representing the possibility and intensity of cardiopulmonary activity. Three different classifiers are applied on these combined features for classification, and the stochastic gradient descent (SGD) is adopted for performance analysis. A radar signal dataset is constructed for various in-vehicle scenarios, including different number of people in a stationary and moving vehicle with several motions. The accuracies reach 97.5% and 97.0% in counting the number of people in the stationary and moving vehicle, and 99.0% in recognizing the motions, demonstrating the effectiveness and robustness of these proposed features. The proposed in-vehicle radar signal dataset is available at https://github.com/yangxiuzhu777/In-Vehicle-Radar-Data .