Influence of the feedback links of connected and automated vehicle on rear-end collision risks with vehicle-to-vehicle communication

A review of naturalistic driving study surrogates and surrogate indicator viability within the context of different road geometries

Advancements in data collection and processing methods have produced large databases containing high quality vehicular data. Despite this, conventional vehicle-vehicle collisions remain difficult to identify due to their rarity. Therefore, there is a need to identify potential collisions given the introduction of these new data collection methods. Surrogate indicators are a popular methods utilised to identify such events, however, the type of surrogate that can be used depends heavily on the type of data collection method. Though most surrogate indicators are used at different road geometries, there is evidence to suggest that some surrogate indicators may perform better than others at a given geometry. This review provides two key contributions to the body of literature. Firstly, a review of kinematic surrogates is put forward, along with a discussion on the whether these surrogates can be contextualised at different road geometries. Secondly, an extensive analysis and discussion of observer-based and video processed surrogate indicators, the collision types they aim to identify and the geometries they have been used at previously were analysed and advantages and disadvantages of the surrogates have been presented for future use. To do this, intersections, highways and roundabouts were selected and divided into geometry subtypes (i.e. three-legged and four-legged intersection) and segments (i.e. approaches to intersections and internal to the intersection) based on the likelihood of crash types and pre-crash manoeuvres occurring in that segment. Due to the lack of research around the use of kinematic triggers at road geometries, it is difficult to advocate for the use of any given trigger over another at a given geometry. Furthermore, it was found that kinematic triggers cannot accurately identify conflicts from naturalistic driving data and require the use of advanced statistical techniques such as machine learning to increase accuracy. A brief analysis of threshold identification techniques was also performed. Several future works have been put forward including the introduction of surrogates which capture conflict severity and the role of surrogate indicators in connected and automated vehicle environments.

Quantifying the automated vehicle safety performance: A scoping review of the literature, evaluation of methods, and directions for future research

Vehicle automation safety must be evaluated not only for market success but also for more informed decision-making about Automated Vehicles' (AVs) deployment and supporting policies and regulations to govern AVs' unintended consequences. This study is designed to identify the AV safety quantification studies, evaluate the quantification approaches used in the literature, and uncover the gaps and challenges in AV safety evaluation. We employed a scoping review methodology to identify the approaches used in the literature to quantify AV safety. After screening and reviewing the literature, six approaches were identified: target crash population, traffic simulation, driving simulator, road test data analysis, system failure risk assessment, and safety effectiveness estimation. We ran two evaluations on the identified approaches. First, we investigated each approach in terms of its input (required data, assumptions, etc.), output (safety evaluation metrics), and application (to estimate AVs' safety implications at the vehicle, transportation system, and society levels). Second, we qualitatively compared them in terms of three criteria: availability of input data, suitability for evaluating different automation levels, and reliability of estimations. This review identifies four challenges in AV safety evaluation: (a) shortcomings in AV safety evaluation approaches, (b) uncertainties in AV implementations and their impacts on AV safety, (c) potential riskier behavior of AV passengers as well as other road users, and (d) emerging safety issues related to AV implementations. This review is expected to help researchers and rulemakers to choose the most appropriate quantification method based on their goals and study limitations. Future research is required to address the identified challenges in AV safety evaluation.

Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon

With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.