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Understanding the dynamic characteristics of surrounding vehicles and estimating the potential risk of mixed traffic can help reliable autonomous driving. However, the existing risk assessment methods are challenging to detect dangerous situations in advance and tackle the uncertainty of mixed traffic. In this paper, we propose a probabilistic driving risk assessment framework based on intention identification and risk assessment of surrounding vehicles. Firstly, we set up an intention<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11432-019-2983-0">doi:10.1007/s11432-019-2983-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x6j4enczenh35p3coq43eh2c6a">fatcat:x6j4enczenh35p3coq43eh2c6a</a> </span>
more »... ation model (IIM) via long short-term memory (LSTM) networks to identify the intention possibility of the surrounding vehicles. Then a risk assessment model (RAM) based on the driving safety field is employed to output the potential risk. Specifically, driving safety field can reflect the coupling relationship of drivers, vehicles, and roads by analyzing their interaction. Finally, an integrated risk evaluation model combining both IIM and RAM is developed to form a dynamic potential risk map considering multi-vehicle interaction. For example, in a typical but challenging lane-changing scenario, an intelligent vehicle can assess its driving status by calculating a risk map in real time that represents the risk generated by the estimated intentions of surrounding vehicles. Furthermore, simulations and naturalistic driving experiments are conducted in the extracted lane-changing scenarios, and the results verify the effectiveness of the proposed model considering lane-changing behavior interaction. Risk assessment has obtained extensive research owing to the application requirements [7, 8] . From the perspective of introducing prior information of multi-vehicle interaction, modeling risk assessment can be classified into two main categories: deterministic risk assessment methods and uncertainty-based risk assessment methods. Deterministic risk assessment methods usually take little account of the intentions of surrounding vehicles and limit in coping with the uncertainty of mixed traffic. They can be roughly classified into distance-based logic methods, time-based logic methods and potential field methods. The distance logic method employs the safety distance in space as the risk evaluation index, and typical representatives include MAZDA model, HONDA model, NHSTA model [9, 10], fixed distance model  , and kinematics model  . The time logic method uses the safe distance in time as the risk evaluation index, such as the time to collision (TTC)  , TTCA , and time headway (THW)  . Most of these methods are based on vehicle kinematics and dynamics theory. Furthermore, the description of driving risk is mostly based on vehicle state/relative motion information. These methods are useful because of their simple parameters and their physical meaning conforms to individuals' subjective feelings. However, these methods are usually limited to one-dimensional (longitudinal or lateral) risk assessment, which is difficult to realize high dimensional uncertainty risk assessment in real traffic, and suffers the limited practical application. Since Khatib  first proposed the artificial potential field (APF) method, the research on describing the driving risk by employing potential field has been continuously developed. Reichardt et al.  proposed an electric field model based on APF to describe the risk distribution of vehicles in traffic environment, thus guiding the safe decision-making. Cao et al. [17, 18] applied the APF method to avoid collision between ego vehicle and other obstacles by establishing an integrated model combining road/vehicle/speed potential field. These APF methods can realize high dimensional risk assessment, achieve better risk-sensitive and accuracy in complex traffic. However, they rarely consider the impact of uncertain factors such as the driver characteristics, vehicle dynamics, road condition and weather. Wang et al.  put forward a unified model of using the concept of driving safety field that considered the comprehensive factors of drivers, vehicles, and roads. The unified model can quantify the driving risks by systematically modeling the coupling relationship of traffic system, but limit in evaluating the traffic elements in the current environment without dynamic consideration of potential risk trends. Further, the above deterministic methods are usually taken as sub-optimal or limited in accuracy for little considering environment-vehicle interaction. Another kind of risk assessment methods considering the prior intention can take more account of environmental uncertainty and interaction of behaviors. The uncertainty-based risk assessment method, also called as the intention-based risk assessment method, generally has two main steps. First, it will estimate the intention of driving behaviors, and then output the collision probability of the future trajectory to calculate the risk degree. These methods usually identify the intention driven by model or data and mainly include support vector machine ( SVM) , Hidden Markov model (HMM) , Bayesian formulation , the Monte Carlo simulation (MC)  and Kalman Filter (KF). Xie et al.  combined the physics-and maneuver-based prediction model via a Bayesian network to make situational assessment, and achieved a high accuracy in lane-changing scenarios. Aoude et al.  employed an intention predictor based on SVM and rapidly-exploring random trees to estimate risk at intersection. Ref.  combined KF and Gaussian distribution to predict the future trajectory distribution and then computed the collision probability, which can achieve good performance in simulation scenes. Meanwhile, long short-term memory (LSTM) has made a series of breakthroughs in speech recognition, machine translation, image captioning, etc., owing to its depth representation ability in time series problem processing. Therefore, a number of research applied LSTM to predict trajectories, and achieved better prediction results [25, 26] . However, although these methods can give more consideration to input the intention for identification, they consider finite factors such as road geometry or driver characteristics. Further, these methods simplify the physical models and limit in describing the coupling mechanism of drivers, vehicles and roads clearly, which constrains their application in specific scenarios and has difficulty to be widely employed in mixed traffic. Therefore, in this paper, we develop an intention-based risk assessment algorithm, as shown in Fig-
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