Automated safety diagnosis of vehicle–bicycle interactions using computer vision analysis

Sayed, Tarek; Zaki, Mohamed H.; Autey, Jarvis

doi:10.1016/j.ssci.2013.05.009

Cited by 153 publications

(82 citation statements)

References 37 publications

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“…Zernetsch et al (2016) collected data at a single intersection for path prediction of a starting cyclists, and Hubert et al (2017) used the same data to find indicators of cyclist starting behavior. Some studies have used naturalistic data to detect and classify critical vehicle-cyclist interactions at intersections (Sayed et al 2013;Vanparijs et al 2015;Cara and de Gelder 2015), while others use simulations to study bicycle motion at intersections Zhang et al 2017).…”

Section: Context Cues For Vru Behaviorsmentioning

confidence: 99%

Context-Based Path Prediction for Targets with Switching Dynamics

et al. 2018

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Anticipating future situations from streaming sensor data is a key perception challenge for mobile robotics and automated vehicles. We address the problem of predicting the path of objects with multiple dynamic modes. The dynamics of such targets can be described by a Switching Linear Dynamical System (SLDS). However, predictions from this probabilistic model cannot anticipate when a change in dynamic mode will occur. We propose to extract various types of cues with computer vision to provide context on the target's behavior, and incorporate these in a Dynamic Bayesian Network (DBN). The DBN extends the SLDS by conditioning the mode transition probabilities on additional context states. We describe efficient online inference in this DBN for probabilistic path prediction, accounting for uncertainty in both measurements and target behavior. Our approach is illustrated on two scenarios in the Intelligent Vehicles domain concerning pedestrians and cyclists, so-called Vulnerable Road Users (VRUs). Here, context cues include the static environment of the VRU, its dynamic environment, and its observed actions. Experiments using stereo vision data from a moving vehicle demonstrate that the proposed approach results in more accurate path prediction than SLDS at the relevant short time horizon (1 s). It slightly outperforms a computationally more demanding state-of-the-art method.

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Section: Context Cues For Vru Behaviorsmentioning

confidence: 99%

Context-Based Path Prediction for Targets with Switching Dynamics

et al. 2018

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“…Changes including signal phase and geometric design were proposed to achieve such goal. Sayed et al [18] developed an automatic safety diagnosis approach for evaluating vehicle-bicycle conflicts at intersections using video analysis. Vehicle-bicycle conflicts as well as vehicle rearend and merging conflicts were successfully identified and examined by their approach.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In such situation, bicycle traffic could have an impact on the operation of vehicles and increase vehicle delays within road segment areas. A review of the literature shows that most previous studies focused on evaluating the interactions between bicycles and vehicles within the intersections in urban areas [16][17][18][19][20][21]. However, such model cannot be directly used for the analysis of street sections.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Interference of Bicycle Traffic on Vehicle Operation on Urban Streets with Bike Lanes

Wang

et al. 2017

Journal of Advanced Transportation

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Many urban streets are designed with on-street bike lanes to provide right-of-way for bicycle traffic. However, when bicycle flow is large, extensive passing maneuvers could occupy vehicle lanes and thus cause interferences to vehicle traffic. The primary objective of this study is to evaluate how bicycle traffic affects vehicle operation on urban streets with bike lanes. Data were collected on six street segments in Nanjing, China. The cumulative curves were constructed to extract traffic flow information including individual bicycle and vehicle speeds and aggregated traffic parameters such as flow and density. The results showed that as bicycle density on bike lanes continuously increases faster bicycles may run into vehicle lanes causing considerable reductions in vehicle speeds. A generalized linear model was estimated to predict the vehicle delay. Results showed that vehicle delay increases as bicycle flow and vehicle flow increase. Number of vehicle lanes and width of bike lane also have significant impact on vehicle delay. Findings of the study are helpful to regions around the world in bike infrastructure design in order to improve operations of both bicycles and vehicles.

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“…The Furthermore, video data represents a permanent record of the traffic events analyzed and can be reviewed and validated. The application of video-based automated conflict analysis was demonstrated to be very useful in conducting various safety analysis such as before-after safety evaluation Tageldin et al 2014), bicycle related data collection and safety diagnosis (Zaki et al 2013;Sayed et al 2013), and pedestrian related safety analysis (Ismail et al 2010;Zaki et al 2013). …”

Section: Conflict Analysismentioning

confidence: 99%