Infrastructure-cooperative algorithm for effective intersection collision avoidance

To guarantee the road safety by avoiding collisions at the intersections is one of the major tasks of intelligent transportation systems (ITSs), which contributes to the minimal fatalities and property loss in crashes. This paper proposes an effective algorithm for infrastructure-cooperative intersection accident pre-warning system with the aid of vehicular communications. The proposed algorithm realizes accurate and efficient collision avoidances through five steps, i.e., defining variable, reasoning the vehicles evolution state, verifying safe driving behavior, assessing risk, and making decision. The critical factors are theoretically analyzed, and a vehicle state evolution model based on the Dynamic Bayesian Networks (DBNs) is established. The efficient risk assessment method based on identifying the dangerous driving behavior at intersection and different collision avoidance strategies are proposed according to the actual situation. Finally, extensive simulations are carried out to verify the performance of the proposal, and simulation results show that the proposed algorithm can effectively detect risk and accurately migrate the collision

Can group-based strategies increase community resilience? Longitudinal predictors of sustained participation in Covid-19 mutual aid and community support groups

Mutual aid groups have been a critical part of the coronavirus disease-2019 (Covid-19) response and continue to address the needs of people in their communities. To understand how mutual aid and similar community support groups can be sustained over time, we test the idea that using group-based strategies initiates psychological trajectories that shape future participation. We conducted a preregistered longitudinal survey among Covid-19 mutual aid and community support volunteers in the United Kingdom (nWave 1 = 600, May 2021; nWave 2 = 299, July–August 2021) who were registered panelists of an independent research organization. Assessments included measures of group-based strategies, collective participation predictors, participation experience, and sustained participation. Volunteers engaged in a wide range of support activities including shopping, emotional support provision, and deliveries. Two group-based strategies—group alliances and group horizontality—longitudinally predicted sustained participation. In addition, sense of community responsibility and burnout were longitudinal predictors of sustained participation. Importantly, predictors of sustained participation diverged for volunteers with different levels of volunteering experience. Our findings highlight group-based strategies as a potential resource for organizers seeking to sustain participation. Use can be tailored depending on the profiles of individual Covid-19 mutual aid volunteers. These findings have significance beyond Covid-19 as they are relevant to sustaining community resilience more generally