Vehicular sensing has reached new heights due to advances in external perception systems enabled by the increasing number and type of sensors in vehicles, as well as the availability of on-board computing. These changes have led to improvements in driver safety and have also created a highly heterogeneous environment of vehicles on the road today in terms of sensing and computing. Using collaborative perception, the information obtained by vehicles with sensing capabilities can be expanded and improved, and older vehicles that lack external sensors and computing capabilities can be informed of potential hazards, opening the opportunity to improve traffic efficiency and safety on the roads. However, achieving real-time collaborative perception is a difficult task due to the dynamic availability of vehicular sensing and computing and the highly variable nature of vehicular communications. To address these challenges, we propose a Heterogeneous Adaptive Collaborative Perception (HAdCoP) framework which utilizes a Context-aware Latency Prediction Network (CaLPeN) to intelligently select which vehicles should transmit their sensor data, the specific individual and collaborative perception tasks, and the amount of computational offloading that should be utilized given information about the current state of the environment. Additionally, we propose an Adaptive Perception Frequency (APF) model to determine the optimal endto-end latency requirement according to the current state of the environment. The proposed CaLPeN model outperforms six implemented comparison models in terms of effective mean average precision (EmAP), beating the next best model’s performance by 5.5% on average when tested on the OPV2V perception dataset using two different combinations of wireless communication conditions and vehicular sensor/computing distributions.