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We present an incremental scalable motion planning algorithm for finding maximally informative trajectories for decentralized mobile robots. These robots are deployed to observe an unknown spatial field, where the informativeness of observations is specified as a density function. Existing works that are typically restricted to discrete domains and synchronous planning often scale poorly depending on the size of the problem. Our goal is to design a distributed control law in continuous domains and an asynchronous communication strategy to guide a team of cooperative robots to visit the most informative locations within a limited mission duration. Our proposed Asynchronous Information Gathering with Bayesian Optimization (AsyncIGBO) algorithm extends ideas from asynchronous Bayesian Optimization (BO) to efficiently sample from a density function. It then combines them with decentralized reactive motion planning techniques to achieve efficient multi-robot information gathering activities. We provide a theoretical justification for our algorithm by deriving an asymptotic no-regret analysis with respect to a known spatial field. Our proposed algorithm is extensively validated through simulation and real-world experiment results with multiple robots. 

This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filter. The tracking phase is a combination of the Kalman filter prediction and an optical flow algorithm to track multiple pedestrians in a scene. We evaluate our framework on a real public driving dataset. Experimental results demonstrate that the proposed method achieves significant performance improvement over a baseline method that solely uses image-based pedestrian detection. 

This paper addresses the problem of detecting pedestrians using an enhanced object detection method. In particular, the paper considers the occluded pedestrian detection problem in autonomous driving scenarios where the balance of performance between accuracy and speed is crucial. Existing works focus on learning representations of unique persons independent of body parts semantics. To achieve a real-time performance along with robust detection, we introduce a body parts based pedestrian detection architecture where body parts are fused through a computationally effective constraint optimization technique. We demonstrate that our method significantly improves detection accuracy while adding negligible runtime overhead. We evaluate our method using a real-world dataset. Experimental results show that the proposed method outperforms existing pedestrian detection methods. 

Connected autonomous vehicles (CAVs) have the potential to deal with the steady increase in road traffic while solving transportation related issues such as traffic congestion, pollution, and road safety. Therefore, CAVs are becoming increasingly popular and viewed as the next generation transportation solution. Although modular advancements have been achieved in the development of CAVs, these efforts are not fully integrated to operationalize CAVs in realistic driving scenarios. This paper surveys a wide range of efforts reported in the literature about the CAV developments, summarizes the CAV impacts from a statistical perspective, explores current state of practice in the field of CAVs in terms of autonomy technologies, communication backbone, and computation needs. Furthermore, this paper provides general guidance on how transportation infrastructures need to be prepared in order to effectively operationalize CAVs. The paper also identifies challenges that need to be addressed in near future for effective and reliable adoption of CAVs.