Crash probability estimation via quantifying driver hazard perception.

Crash probability estimation is an important method to predict the potential reduction of crash probability contributed by forward collision avoidance technologies (FCATs). In this study, we propose a practical approach to estimate crash probability, which combines a field operational test and numerical simulations of a typical rear-end crash model. To consider driver hazard perception characteristics, we define a novel hazard perception measure, called as driver risk response time, by considering both time-to-collision (TTC) and driver braking response to impending collision risk in a near-crash scenario. Also, we establish a driving database under mixed Chinese traffic conditions based on a CMBS (Collision Mitigation Braking Systems)-equipped vehicle. Applying the crash probability estimation in this database, we estimate the potential decrease in crash probability owing to use of CMBS. A comparison of the results with CMBS on and off shows a 13.7% reduction of crash probability in a typical rear-end near-crash scenario with a one-second delay of driver's braking response. These results indicate that CMBS is positive in collision prevention, especially in the case of inattentive drivers or ole drivers. The proposed crash probability estimation offers a practical way for evaluating the safety benefits in the design and testing of FCATs.

Driving risk assessment using near-crash database through data mining of tree-based model.

This paper considers a comprehensive naturalistic driving experiment to collect driving data under potential threats on actual Chinese roads. Using acquired real-world naturalistic driving data, a near-crash database is built, which contains vehicle status, potential crash objects, driving environment and road types, weather condition, and driver information and actions. The aims of this study are summarized into two aspects: (1) to cluster different driving-risk levels involved in near-crashes, and (2) to unveil the factors that greatly influence the driving-risk level. A novel method to quantify the driving-risk level of a near-crash scenario is proposed by clustering the braking process characteristics, namely maximum deceleration, average deceleration, and percentage reduction in vehicle kinetic energy. A classification and regression tree (CART) is employed to unveil the relationship among driving risk, driver/vehicle characteristics, and road environment. The results indicate that the velocity when braking, triggering factors, potential object type, and potential crash type exerted the greatest influence on the driving-risk levels in near-crashes.

The discovery of dinotefuran: a novel neonicotinoid.

Dinotefuran (MTI-446: (RS)-1-methyl-2-nitro-3-(tetrahydro-3-furylmethyl)guanidine) is a new neonicotinoid commercialized by Mitsui Chemicals. Research led to this novel neonicotinoid by the removal of the chloropyridine or chlorothiazole ring that had been considered as indispensable for neonicotinoides. The research advanced as follows; (1) selection of acetylcholine for the lead compound, (2) recognition of the insecticidal advantages of 3-methoxypropyl compounds, (3) synthesis of (+/-)-tetrahydro-3-furylmethyl compounds by cyclization of the 3-methoxypropyl moiety. It resulted in dinotefuran which has a (+/-)-tetrahydro-3-furylmethyl moiety instead of a halogenated aromatic heterocyclic ring, and belongs to the third-generation neonicotinoids (sub-class: furanicotinyl compounds).