Multi‐object tracking in autonomous driving is a non‐linear problem.To better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's state.In the association stage,t...Multi‐object tracking in autonomous driving is a non‐linear problem.To better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's state.In the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for matching.With the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous trajectories.We conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.展开更多
Air pollution is a major contributor to the global disease burden,especially affecting respiratory and cardiovascular health.However,physical activity is associated with improved lung function,a slower decline in lung...Air pollution is a major contributor to the global disease burden,especially affecting respiratory and cardiovascular health.However,physical activity is associated with improved lung function,a slower decline in lung function,and lower mortality.The public is more likely to be exposed to air pollution during outdoor physical activity.However,studies on how long-term and short-term exposure to air pollution interacts with physical activity yield inconsistent results,and the thresholds for air pollution and physical activity remain unclear.Thus,more studies are needed to provide sufficient evidence to guide the public to safely engage in outdoor physical activity when exposed to air pollution.展开更多
文摘Multi‐object tracking in autonomous driving is a non‐linear problem.To better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's state.In the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for matching.With the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous trajectories.We conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.
基金supported by the National Key Research and Development Program of China(grant number 2022YFC3702604)the National Natural Science Foundation of China(grant number 41977374).
文摘Air pollution is a major contributor to the global disease burden,especially affecting respiratory and cardiovascular health.However,physical activity is associated with improved lung function,a slower decline in lung function,and lower mortality.The public is more likely to be exposed to air pollution during outdoor physical activity.However,studies on how long-term and short-term exposure to air pollution interacts with physical activity yield inconsistent results,and the thresholds for air pollution and physical activity remain unclear.Thus,more studies are needed to provide sufficient evidence to guide the public to safely engage in outdoor physical activity when exposed to air pollution.
