Forecasting TimetoCollision from Monocular Video: Feasibility, Dataset, and Challenges We explore the possibility of using a single monocular camera to forecast the time to collision between a suitcaseshaped robot being pushed by its user and other nearby pedestrians. We develop a purely imagebased deep learning approach that directly estimates the time to collision without the need of relying on explicit geometric depth estimates or velocity information to predict future collisions. While previous work has focused on detecting immediate collision in the context of navigating Unmanned Aerial Vehicles, the detection was limited to a binary variable (i.e., collision or no collision). We propose a more finegrained approach to collision forecasting by predicting the exact time to collision in terms of milliseconds, which is more helpful for collision avoidance in the context of dynamic path planning. To evaluate our method, we have collected a novel dataset of over 13,000 indoor video segments each showing a trajectory of at least one person ending in a close proximity (a near collision) with the camera mounted on a mobile suitcaseshaped platform. Using this dataset, we do extensive experimentation on different temporal windows as input using an exhaustive list of stateoftheart convolutional neural networks (CNNs). Our results show that our proposed multistream CNN is the best model for predicting time to nearcollision. The average prediction error of our time to nearcollision is 0.75 seconds across the test videos. The project webpage can be found at https:aashi7.github.ioNearCollision.html.

Rapid Collision Detection for Multicopter Trajectories We present a continuoustime collision detection algorithm for quickly detecting whether certain polynomial trajectories in time intersect with convex obstacles. The algorithm is used in conjunction with an existing multicopter trajectory generation method to achieve rapid, obstacleaware motion planning in environments with both static convex obstacles and dynamic convex obstacles whose boundaries do not rotate. In general, this problem is difficult because the presence of convex obstacles makes the feasible space of trajectories nonconvex. The performance of the algorithm is benchmarked using Monte Carlo simulations, and experimental results are presented that demonstrate the use of the method to plan collisionfree multicopter trajectories in milliseconds in environments with both static and dynamic obstacles.