SCIENCE CHINA Information Sciences, Volume 63 , Issue 9 : 190203(2020) https://doi.org/10.1007/s11432-019-2983-0

A probabilistic risk assessment framework considering lane-changing behavior interaction

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  • ReceivedDec 1, 2019
  • AcceptedJul 7, 2020
  • PublishedAug 17, 2020


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 identification 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.


This work was supported by the Major Project of National Natural Science Foundation of China (Grant No. 61790561), National Science Fund for Distinguished Young Scholars (Grant No. 51625503), Intel Collaborative Research Institute on Intelligent and Automated Connected Vehicles (ICRI-IACV), the Joint Laboratory for Internet of Vehicle, and Ministry of Education - China Mobile Communications Corporation. We would also like to express our great thanks to the Ph.D. candidates, Hui XIONG and Yang LI, who participated in the discussion and optimized the study.


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  • Figure 1

    (Color online) The framework of the comprehensive risk evaluation model. The combined model is composed of an intention identification model (IIM) and a risk assessment model (RAM). The IIM outputs the intention possibilities to the RAM. The RAM describes the dynamic magnitude and influence range of driving risk. Finally, a predictive risk map is generated to quantify the potential risk.

  • Figure 2

    (Color online) The structure of IIM. The input network includes a series of LSTM networks, and the output network generates the intention probabilities.

  • Figure 3

    (Color online) Schematic diagram of the predicted vehicle and its surrounding environment. The predicted vehicle $v_e$ is surrounded by six other vehicles $(V_{h1}-V_{h6})$, and the potential intentions of these vehicles are described by the lines with different colors. Each vehicle has its potential influence range shown as an ellipse in the figure.

  • Figure 4

    (Color online) Procedure of lane change. Three processes are defined in this figure according to the surrounding vehicle information and the trajectory of the predicted vehicle, where $\theta_s$ and $\theta_e$ are the heading angle threshold of the lane change start and end points, respectively.

  • Figure 5

    (Color online) The confusion matrix of intention identification. When the actual intention is keeping straight, the identification result turns to be the worst compared with recognizing the intention of turning left or turning right for two potential lanes, which will disturb the recognition process.

  • Figure 6

    (Color online) Elliptic constraint effect of road marking on traffic risk. The two dotted lines have the function of constraining vehicle $j$ to follow the centerline of its own lane, and hence when vehicle $j$ keeps straight, the influence range will turn to be in its own lane without any intention to change lane.

  • Figure 7

    (Color online) Force distribution of driving safety field under traffic marking restriction.

  • Figure 8

    (Color online) The predictive driving risk map. Combining IIM and RAM, a predictive risk map with intention possibility can describe the influence range and trend. In this example, we output the predicted vehicle with a 20% lane-keeping probability ($p_3=0.2$) and an 80% lane-changing probability ($p_1=0.8,p_2=0$) predicted by the IIM. Therefore, the distribution and magnitude of the field force are also proportional to 20% and 80% in the straight-line direction and the lane change direction.

  • Figure 9

    (Color online) Two different examples of driving risk maps. If there is no behavior prediction, it is assumed that all vehicles in the traffic map are stable and do not change their states suddenly. As time goes by, the behaviors of surrounding vehicles will change suddenly, resulting in possible collision risks when there is no early warning. Introducing the intention identification will ensure the reliability of planning and control in the subsequence stage. (a) An example of ‘Perception-Assessment-Planning' architecture; (b) an example of ‘erception-Prediction-Assessment-Planning' architecture.

  • Figure 10

    (Color online) Trajectory prediction of a surrounding vehicle.

  • Figure 11

    (Color online) Traffic risk maps of naturalistic driving scenarios. The top picture of each subgraph is the real scenario extracted from the highD Dataset. The red block in the figure represents the moving vehicle, the yellow label indicates the speed and ID of each vehicle, and the color depth in the map represents the risk intensity affected by the vehicle's speed, quality, and relative position with surrounding vehicles. (a) Naturalistic lane-keeping scenario; protectłinebreak (b) naturalistic cut-in scenario.

  • Table 1  

    Table 1Performance evaluation of different methods

    Intention Precision Recall Accuracy
    Turning left 0.925 0.903 0.884 0.833 0.874 0.832
    Keeping straight 0.785 0.716 0.859 0.828
    Turning right 0.927 0.907 0.880 0.835

    Algorithm 1 Risk assessment based on IIM and RAM

    Initialize $m_j,~r_{\rm~max},~v_j,~\theta_{ji},~k_{x,d},~k_{y,d},~A_1A_2,~B_1B_2,~r_0$;

    Get the initial state of the predicted vehicle $V_e^{(t)}$ and surrounding vehicles $V_{hi}^{(t)~}$;

    Set up the IIM;

    for $i=1$ to $n$ (traffic participants)

    Input the interaction state: $I^{(t)}=[V_h^{(t)~},S^{(t)~}~]~~,t=(T-T_p,\ldots~,T-1,T)$;

    Calculate the intention probability by using the Softmax function;

    Output the probability of the IIM: $p_m=P(l_m|I),\Omega=(p_1,p_2,p_3)$;

    end for

    for $m=1$ to $3$

    Define the driving risk force: $F_{ji,0}=E_{j,0}~(\frac{r_0}{k_{x,0}~x_{ji}^2+k_{y,0}~y_{ji}^2~}-\frac{1}{r_{\rm~max}~})$;

    Define the risk range: $r_{ji}=\frac{r'_{ji}}{\sqrt{k_{x,d} {\rm cos}^2\theta_{ji}+k_{y,d} {\rm sin}^2 \theta_{ji}}}$;

    Calculate the predictive driving risk force in this direction $F_{ki,m}=p_m~F_{ji,0}$;

    Output the predictive risk map in this direction;

    end for

    Calculate the total predictive driving risk force: $F_{ki}=\sum_{m=1}^3F_{ki,m}$;

    Output the total predictive risk map;

    Define the threshold force $F_{\rm~th}$ of active assistance based on the existed algorithm [19];

    if $F_{ki}>F_{\rm~th}$ then

    Output(“Danger from the surrounding vehicle" warning to vehicle $j$);


    Output(“Safe driving" feedback to vehicle $j$);

    end if

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