The performance of the current collision avoidance systems in Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) can be drastically affected by low light and adverse weather conditions. Collisions with large animals such as deer in low light cause significant cost and damage every year. In this paper, we propose the first AI-based method for future trajectory prediction of large animals and mitigating the risk of collision with them in low light. In order to minimize false collision warnings, in our multi-step framework, first, the large animal is accurately detected and a preliminary risk level is predicted for it and low-risk animals are discarded. In the next stage a multi-stream CONV-LSTM-based encoder-decoder framework is designed to predict the future trajectory of the potentially high-risk animals. The proposed model uses camera motion prediction as well as the local and global context of the scene to generate accurate predictions. Furthermore, this paper introduces a new dataset of FIR videos for large animal detection and risk estimation in real nighttime driving scenarios. Our experiments show promising results of the proposed framework in adverse conditions. Our code is available online1.

Fingernail imaging has been proven to be effective in prior works [1], [2] for estimating the 3D fingertip forces with a maximum RMS estimation error of 7%. In the current research, fingernail imaging is used to perform unconstrained grasp force measurement on multiple fingers to study human grasping. Moreover, two robotic arms with mounted cameras and a visual tracking system have been devised to keep the human fingers in the camera frame during the experiments. Experimental tests have been conducted for six human subjects under both constrained and unconstrained grasping conditions, and the results indicate a significant difference in force collaboration among the fingers between the two grasping conditions. Another interesting result according to the experiments is that in comparison to constrained grasping, unconstrained grasp forces are more evenly distributed over the fingers and there is less force variation (more steadiness) in each finger force. These results validate the importance of measuring grasp forces in an unconstrained manner in order to study how humans naturally grasp objects.

Control of the exerted force on objects as well as the position of the actuator during a cell characterization operation has an essential role in achieving an efficient performance. In such operations, any undesired applied force could degrade the efficiency or make damages to the object. A challenging problem associated with application to cell characterization using a piezoelectric actuator is to design a suitable force-position switching control law and also to deal with nonlinear hysteretic behavior of such actuators. The main concern in this operation is to provide a smooth switching transition between position and force control modes. A modified Prandtl-Ishlinskii (PI) operator and its inverse are utilized for both identification and real time compensation of the hysteresis nonlinear behavior. By proposing an appropriate force-position switching control approach, the smooth transition between these two modes is guaranteed. The stability of each controller as well as the switching system is demonstrated analytically. Experimental results depict that the proposed approach achieves precise force-position control within each modes and also the switching remains smooth.