Analysis of pre-crash scenarios and contributing factors for autonomous vehicle crashes at intersections.

Intersections are high-risk locations for autonomous vehicles (AVs). Crash causation analysis based on pre-crash scenarios can provide new insight into these crashes that can lead to effective countermeasures, but there are significant differences in pre-crash scenarios between autonomous and conventional vehicles, and inadequate AV data has put limits on research. The association rule method, however, can yield useful results despite these limits. This study therefore aims to use the method with pre-crash scenarios to understand the characteristics and contributing factors of AV crashes at intersections from the latest 5-year AV crash data. Analysis of 197 AV crashes at intersections revealed 30 types of pre-crash scenarios. The rear-end crash (58.88%) and lane change crash (16.24%) were the most frequently occurring scenarios for AVs. The proportion of AVs being rear-ended by conventional vehicles was 58.38%. The main contributing factors of these two most common AV scenarios were identified by association rules and crash causes were analyzed from the perspective of AV decision-making. The main factors contributing to the AV rear-end scenario were location outside the intersection in the intersection-related area, traffic signal control, autonomous engaged mode, mixed-use or public land, and weekdays, while those for lane change scenarios were on-street parking and the time of 8:00 a.m. Important causes of rear-end crashes attributable to the AV were inadequate stop and deceleration decisions by the AV's automated driving system (ADS) and insufficient collision avoidance decisions in lane change crashes. Identification of the pre-crash characteristics and contributing factors provide new insight into AV crash causation and can be used in the determination of the AV's operational design domain and the development and optimization of the AV's ADS at intersections. These findings can also play a role in guiding traffic safety agencies to discover AV hotspots and propose AV management regulations.

Developing an improved automatic preventive braking system based on safety-critical car-following events from naturalistic driving study data.

In public road tests of autonomous vehicles in California, rear-end crashes have been the most common type of crash. Collision avoidance systems, such as autonomous emergency braking (AEB), have provided an effective way for autonomous vehicles to avoid collisions with the lead vehicle, but to avert false alarms, AEB tends to apply late and hard brake only if a collision becomes unavoidable. Automatic preventive braking (APB) is a new collision avoidance method used in Mobileye's Responsibility-Sensitive Safety (RSS) model that aims to reduce crashes with a milder brake and decreased impact on traffic flow, but APB's safety performance is inferior to that of AEB. This study therefore proposes three safety improvement strategies for APB, the addition of response time, safety buffer, and minimum following distance; and combines them in different ways into four improved APB systems, IP1-IP4. Simulating car-following safety-critical events (SCEs) extracted from the Shanghai Naturalistic Driving Study in MATLAB's Simulink, the safety performance, conservativeness, and driving comfort of the four systems were evaluated and compared with the original APB system, two AEB systems, and human drivers. The results show that 1) IP4, the system that integrated all three strategies, outperformed the baseline APB and IP1-IP3 and prevented all SCEs from becoming crashes; 2) IP4 was slightly more conservative than AEB, but less conservative than RSS; 3) APB's jerk-bounded braking profile improved driving comfort; and 4) higher deceleration was found in the two AEB systems (both 8.1 m/s2) than in IP4 (6.7 m/s2), but they failed to prevent all crashes. Our proposed APB system, IP4, can provide safe, efficient, and comfortable braking for AVs in car-following SCEs, and has the potential to be practically applied in vehicle collision avoidance systems.

Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology.

Data-based research approaches to generate crash scenarios have mainly relied on conventional vehicle crashes and naturalistic driving data, and have not considered differences between the autonomous vehicle (AV) and conventional vehicle crashes. As the AV's presence on roadways continues to grow, its crash scenarios take on new importance for traffic safety. This study therefore obtained crash patterns using the United States Department of Transportation pre-crash scenario typology, and used statistical analysis to determine the differences between AV and conventional vehicle pre-crash scenarios. Analysis of 122 AV crashes and 2084 conventional vehicle crashes revealed 15 types of scenario for AVs and 26 for conventional vehicles. The two groups showed differences in type of scenario, and differed in the proportion of crashes when the scenario was the same. The most frequent AV pre-crash scenarios were rear-end collisions (52.46%) and lane change collisions (18.85%), with the proportion of AVs rear-ended by conventional vehicles occurring with a frequency 1.6 times that of conventional vehicles. An in-depth crash investigation was conducted of the characteristics and causes of four AV pre-crash scenarios, summarized from the perspectives of perception and path planning. The perception-reaction time (PRT) difference between AVs and human drivers, AV's inaccurate identification of the intention of other vehicles to change lanes, and AV's insufficient path planning combining time and space dimensions were found to be important causes for the AV crashes. By increasing understanding of the complex characteristics of AV pre-crash scenarios, this analysis will encourage cooperation with vehicle manufacturers and AV technology companies for further study of crash causation toward the goals of improved test scenario construction and optimization of the AV's automated driving system (ADS).

Investigate moped-car conflicts in China using a naturalistic driving study approach.

PROBLEM: Mopeds are a popular transportation mode in Europe and Asia. Moped-related traffic accidents account for a large proportion of crash fatalities. To develop moped-related crash countermeasures, it is important to understand the characteristics of moped-related conflicts. METHOD: Naturalistic driving study data were collected in Shanghai, China from 36 car drivers. The data included 2,878h and 78,296km driven from 13,149 trips. Moped-car conflicts were identified and examined from the passenger car driver's perspective using kinematic trigger algorithms and manual video reduction. RESULTS: A total of 119 moped-car conflicts were identified, including 74 high g-force conflicts and 45 low g-force events. These conflicts were classified into 22 on-road configurations where both similarities and differences were found as compared to Western Countries. The majority of the conflicts occurred on secondary main roads and branch roads. Hard braking was the primary response that the car drivers made to these conflicts rather than hard steering. DISCUSSIONS: The identified on-road vehicle-moped conflict configurations in Shanghai, China may be attributed to the complicated traffic environment and risky behavior of moped riders. The lower prevalence of hard steering in Shanghai as compared to the United States may be due to the lower speeds at event onsets or less available steering space, e.g., less available shoulder area on Chinese urban roads. CONCLUSIONS: The characteristics of moped-car conflicts may impact the design of active safety countermeasures on passenger cars. The pilot data from Shanghai urban areas suggest that countermeasures developed for China may require some modifications to those developed for the United States and European countries, although this recommendation may not be conclusive given the small sample size of the study. Future studies with large samples may help better understand the characteristics of moped-car conflicts.

Indirect effect of corticotropin-releasing hormone receptor 1 gene variation on negative emotionality and alcohol use via right ventrolateral prefrontal cortex.

Variations in the corticotropin-releasing hormone receptor 1 (CRHR1) gene have been found to interact with stress in modulating excessive alcohol consumption. However, the neural mechanisms through which CRHR1 influences this risk in humans is largely unknown. This study examined the influence of an intronic CRHR1 gene variant, rs110402, on brain responses to negative emotional words, negative emotional traits, and alcohol use in adolescents and young adults at high risk for alcoholism. Childhood stress was investigated as a potential moderator. Using functional magnetic resonance imaging, we found that a region in the right ventrolateral prefrontal cortex (rVLPFC) was more engaged during negative emotional word processing in G homozygotes than in A allele carriers (p(FWE corrected) < 0.01, N = 77). Moreover, an indirect effect of genotype on negative emotionality via rVLPFC activation (p < 0.05, N = 69) was observed, which was further moderated by childhood stress (p < 0.05, N = 63). Specifically, with low childhood stress, G homozygotes exhibited lower levels of negative emotionality associated with greater rVLPFC activation, suggesting that the rVLPFC is involved in reappraisal that neutralizes negative emotional responses. In addition, we found that genotype indirectly modulated excessive alcohol consumption (p < 0.05, N = 69). Specifically, G homozygotes showed greater rVLPFC activation and had lower levels of negative emotionality, which were associated with fewer binge-drinking days and fewer alcohol related problems. This work provides support for a model in which CRHR1 gene variation modulates the risk of problem drinking via an internalizing/negative affect pathway involving rVLPFC and reappraisal of negative emotion.

Neural basis of semantic and syntactic interference in sentence comprehension.

According to the cue-based parsing approach (Lewis, Vasishth, & Van Dyke, 2006), sentence comprehension difficulty derives from interference from material that partially matches syntactic and semantic retrieval cues. In a 2 (low vs. high semantic interference)×2 (low vs. high syntactic interference) fMRI study, greater activation was observed in left BA44/45 for high versus low syntactic interference conditions following sentences and in left BA45/47 for high versus low semantic interference conditions following comprehension questions. A conjunction analysis showed BA45 associated with both types of interference, while BA47 was associated with only semantic interference. Greater activation was also observed in the left STG in the high interference conditions. Importantly, the results for the LIFG could not be attributed to greater working memory capacity demands for high interference conditions. The results favor a fractionation of the LIFG wherein BA45 is associated with post-retrieval selection and BA47 with controlled retrieval of semantic information.