Impacts of advanced driver assistance systems on commercial truck driver behaviour performance using naturalistic data

Abstract Traffic crashes tend to be more serious when commercial trucks are involved. And an increasing number of commercial vehicles are equipped with advanced driver assistance systems (ADAS) for traffic safety improvement. To this end, this study investigated the impacts of ADAS with respect to forward collision warning (FCW), urban forward collision warning (UFCW), lane departure warning (LDW), headway monitoring and warning (HMW), and speed limit indicator (SLI) on commercial truck drivers' behaviours using naturalistic data. Participants experienced two different test scenarios with two different ADAS warning configurations: Visual and audio alerts in the inactive state and in the active state. After activating alerts, 62%, 35%, 69%, 81% and 73% of drivers received less warnings in terms of FCW, UFCW, LDW, HMW and SLI respectively; the average of FCW, UFCW, LDW, HMW and SLI respectively declined 22%, 13%, 28%, 45% and 15%, but median changed little except for LDW and HMW. Then, the further analysis results by the one‐way analysis of variance (ANOVA) method revealed that all warnings except UFCW had positive effect on commercial truck drivers' behaviour performance. The findings provide important references for commercial truck driver training and supervision in the age of advanced technology

Bayesian Network-Based Knowledge Graph Inference for Highway Transportation Safety Risks

Accurate inference of knowledge about highway transportation safety risks forms a crucial aspect of building a knowledge graph. Based on the data related to highway transportation accidents, this study has developed a Bayesian network model. The initial identification of the network nodes is through expert scoring. The network structure is then constructed by utilizing the prior expert knowledge and K2 greedy search algorithm. Later, the network parameters are trained via the expectation-maximization (EM) algorithm. Finally, knowledge about highway transportation safety risks is inferred using the junction tree algorithm. A comparison is made between the trained conditional and actual probabilities during the network parameter training to verify the validity of the proposed model that accords with expert experience, thereby proving the model validity. Further, its main “causal chain” is inferred to be an improper emergency response-human failure-accident occurrence, where the probability of driver failure is 82%, and the probability of accident occurrence is 68% by taking “a certain road traffic accident” as an example. There is consistency between the inference results and the actual accident sequence that suggests the effectiveness of the proposed knowledge inference method