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Cooperative perception, or collective perception (CP), is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of vehicle-to-X (V2X) communication, thereby achieving improved efficiency and safety in road transportation. In this paper, we present our recent progress on the development of a connected and automated vehicle (CAV) and intelligent roadside unit (IRSU). The main contribution of the work lies in investigating and demonstrating the use of CP service within intelligent infrastructure to improve awareness of vulnerable road users (VRU) and thus safety for CAVs in various traffic scenarios. We demonstrate in experiments that a connected vehicle (CV) can “see” a pedestrian around the corners. More importantly, we demonstrate how CAVs can autonomously and safely interact with walking and running pedestrians, relying only on the CP information from the IRSU through vehicle-to-infrastructure (V2I) communication. This is one of the first demonstrations of urban vehicle automation using only CP information. We also address in the paper the handling of collective perception messages (CPMs) received from the IRSU, and passing them through a pipeline of CP information coordinate transformation with uncertainty, multiple road user tracking, and eventually path planning/decision-making within the CAV. The experimental results were obtained with manually driven CV, fully autonomous CAV, and an IRSU retrofitted with vision and laser sensors and a road user tracking system.

A new particle-based distribution truncation method with a durationdependent hidden semi-Markov model for non-intrusive appliance load monitoring is presented. Unlike earlier works, the approach keeps track of a set of states without prematurely pruning away intermediately ranked states. It also enables appliance state duration characteristics to be incorporated in a straightforward manner. Results show that the approach outperforms both the Viterbi algorithm and conventional particle-filtering methods.Introduction: Non-intrusive appliance load monitoring (NIALM) uses aggregate information to mathematically infer the power consumption of contributing appliances [1]. Past works in the field typically employed methods to find the best point estimates at each time step. Prime examples include the maximum a posteriori probability [2] and the Viterbi algorithm [3] which only keep the best previous state for each possible current state value. This is undesirable because the most probable previous state does not necessarily correspond to the true state. In addition, measurements obtained so far may not provide sufficient information for good decisions to be made. Very recently, particle-filtering (PF) with a factorial hidden Markov model (FHMM) for appliance state tracking was proposed [4]. However, PF is encumbered by the problem of low particle diversity [5]. In the case of NIALM, where a given observed aggregate signal can be explained by many different combinations of component signals, this problem is wasteful considering that particle slots are premium and identical offsprings (with similar state sequence) offer no benefits but take up particle slots that could otherwise be occupied by particles with different state sequences. In addition, the use of FHMM implicitly assumes that durations of all appliance states are geometrically distributed, which in reality is clearly not the case.In this Letter, we propose a new particle-based distribution truncation (PDT) method with a duration-dependent (DD) state transition model which does away with such limitations. Our model is inspired by Vaseghi [6], and unlike typical hidden semi-Markov models [7] our formulation allows appliance state duration characteristics to be easily incorporated into the state transition model. The resulting inference stage is computationally efficient with no explicit search over all possible state durations.Tests were performed on a public dataset of real homes [8]. Results show that our approach performs better than the Viterbi algorithm and the PF method as outlined in [4].

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