A forced lane-changing decision-making method for autonomous driving based on dynamic continuous game

Abstract

The present invention relates to a method for making decisions about forced lane changes in an autonomous driving system based on dynamic continuous gaming, and belongs to the field of intelligent connected vehicle technology. The method comprises the following steps: obtaining motion state information of the vehicle and surrounding vehicles in a forced lane change scenario through on-board sensors and vehicle-to-vehicle communication; calculating collision boundaries; generating an initial decision based on a non-cooperative non-zero-sum dynamic game; optimizing the initial strategy using a game strategy based on information continuity; and outputting a final forced lane change decision on the condition that the strategy optimized using the game decision based on information continuity is maintained for three consecutive cycles. The present invention is a method for making decisions about forced lane changes in an autonomous driving system based on dynamic gaming, which takes decision continuity into consideration. The method uses a non-cooperative non-zero-sum dynamic game to construct a decision model, thereby improving driving safety, comfort, and efficiency. The method uses a dynamic game algorithm based on decision continuity to enhance decision adaptability and predictability, ensure the stability and consistency of decision output, and reduce uncertainty and risk.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent connected vehicles, and in particular to an automatic driving forced lane change decision method based on dynamic continuous game. Background Art

[0002] Decision-making is particularly crucial for autonomous driving, as it integrates sensory information to develop safe and efficient driving strategies, including but not limited to lane-keeping and lane-changing maneuvers. Lane changing accounts for a significantly higher proportion of serious accidents than speeding and rear-end collisions. More importantly, the proportion of fatal accidents related to lane changing is on the rise, highlighting the urgency and importance of optimizing lane-changing decisions for autonomous driving.

[0003] Compared to free lane changes, the mandatory nature of forced lane changes exacerbates traffic congestion, increases the risk of accidents, and causes greater traffic disruption. Effective forced lane change decisions can not only significantly reduce traffic accidents, but also improve road efficiency and alleviate congestion.

[0004] Game theory-based methods have shown significant advantages in multi-vehicle interaction, strategy optimization and equilibrium solution, especially in complex traffic scenarios. In contrast, although rule-based and learning-based methods are also very effective in many cases, they are not as flexible and accurate as game theory-based methods when dealing with dynamic interactions and seeking strategy equilibrium in some unknown scenarios. However, existing game theory-based methods only consider static or discrete game models to simulate the decision-making process between vehicles in forced lane change scenarios, while ignoring the continuous decision-making process under environmental changes, dynamic interactions and multiple factors. Therefore, the present invention proposes an automatic driving forced lane change decision-making method based on dynamic continuous game to cope with complex and changeable decision-making environments, thereby improving the effectiveness of the decision-making process. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides an autonomous driving forced lane change decision-making method based on dynamic continuous game, which solves the traffic conflict problem in forced lane change scenarios, while ensuring the safety, comfort and efficiency of autonomous driving vehicles, and improving the dynamic optimization capability of decision-making.

[0006] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions:

[0007] A method for automatic driving forced lane change decision-making based on dynamic continuous game, comprising the following steps:

[0008] Step 1: Obtaining motion state information of the vehicle and surrounding vehicles in a forced lane change scenario through on-board sensors and vehicle-to-vehicle communication. The motion state information includes the vehicle's position, velocity, and acceleration.

[0009] Step 2: Use the unequal-diameter three-circle contour method to model the vehicle boundary and calculate the collision boundary between the vehicle and any surrounding vehicle;

[0010] Step 3: Generate an initial decision based on a non-cooperative non-zero-sum dynamic game, which includes the following steps:

[0011] Step 31: Construct a forced lane change decision model, which includes two participants: the ego vehicle and the vehicle behind in the target lane, and formulate game optional strategies for the two participants;

[0012] Step 32: Establish the driving safety benefit function, ride comfort benefit function, and traffic efficiency benefit function for the ego vehicle and the vehicle behind it in the target lane, respectively. Take the weighted sum of these three benefit functions to obtain the total benefit corresponding to the ego vehicle and the vehicle behind it in the target lane.

[0013] Step 3: The ego vehicle and the vehicle behind it in the target lane perform a non-cooperative non-zero-sum dynamic game based on the total benefits of different strategy combinations to generate an initial decision;

[0014] Step 4: Optimize the initial strategy using a game strategy based on information continuity;

[0015] Step 5: Under the condition that the strategy optimized by the game decision based on information continuity is maintained for three consecutive cycles, the final forced lane change decision is output.

[0016] The present invention provides a dynamic game decision-making method for forced lane change in autonomous driving that takes decision continuity into account. Compared with the prior art, this method has the following advantages:

[0017] (1) The present invention introduces a non-cooperative, non-zero-sum dynamic game and constructs a forced lane-changing decision model that takes into account driving safety, riding comfort, and traffic efficiency. This model enables more intelligent decision-making while ensuring safety.

[0018] (2) The present invention uses a dynamic game algorithm based on decision continuity to capture dynamic changes in information flow to enhance the adaptability and predictability of decision-making; in addition, it can maintain the stability and consistency of decision output to reduce uncertainty and risk in the decision-making process;

[0019] (3) The present invention optimizes the traditional equal-diameter three-circle contour method and takes into account the vehicle's driving speed and the driver's interaction mode during the contour expansion process, which can more accurately reflect the dynamic and personalized driving state. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of the automatic driving forced lane change decision method according to the present invention;

[0021] Figure 2This is a schematic diagram of a forced lane change scenario;

[0022] Figure 3 This is a schematic diagram of the unequal diameter three-circle contour method;

[0023] Figure 4 Schematic diagram of non-cooperative non-zero-sum dynamic game;

[0024] Figure 5 Schematic diagram of the game strategy based on information continuity;

[0025] Figure 6 Schematic diagram of the game strategy based on the continuity of output results. DETAILED DESCRIPTION

[0026] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments.

[0027] like Figure 1 As shown, one embodiment of the present invention proposes an automatic driving forced lane change decision method based on dynamic continuous game, which includes the following steps 1 to 5.

[0028] Step 1: Obtain the motion status information of the vehicle and surrounding vehicles in the forced lane change scenario through on-board sensors and vehicle-to-vehicle communication, and identify the forced lane change scenario.

[0029] like Figure 2 As shown in the figure, the vehicles involved in the forced lane change scenario include the ego vehicle (EV), the preceding vehicle in the target lane (LV), the following vehicle in the target lane (FV), the vehicle in the third lane (TV), and the stationary obstacle vehicle (BV). When the EV detects an obstacle ahead, it determines whether it is a stationary obstacle based on its motion state information. If a stationary obstacle vehicle (BV) is identified, it is considered to have entered a lane change scenario and generates a lane change intention. The EV must select an appropriate gap and merge safely, comfortably, and efficiently into lane 2 without colliding with the BV or other surrounding vehicles (FV, TV, LV). During this process, the EV needs to obtain motion state information such as the position, velocity, and acceleration of other surrounding vehicles.

[0030] Step 2: Calculate the forced lane change collision area and collision boundary.

[0031] In order to reduce the parameters describing the vehicle's geometric outline while maximally reflecting the vehicle's geometric shape, this embodiment uses the three-circle contour method to model the vehicle's boundaries. The three-circle contours link the vehicle's center of mass coordinates with the centers of the three circles, thereby calculating and determining the collision boundary between the vehicle and any surrounding vehicle, and obtaining the collision area analysis results. During vehicle driving, the areas of interest in different directions vary in size, and the safety of the forward area is more important. Therefore, the optimized equal-diameter three-circle method is used to expand the circular outline in front of the vehicle, as shown in Figure 1. Figure 3 As shown, the formula for expanding the circular outline in front of the vehicle is as follows:

[0032]

[0033] Among them, R e is the radius of the circle in front of the vehicle; ρ is a fixed coefficient of 0.1; v is the vehicle speed; R safe is the safety factor, which is related to the driver's interaction mode; R is the radius of the circle behind the vehicle and the circle in the middle of the vehicle. The centers of the circle in front of the vehicle, the circle in the middle of the vehicle, and the circle behind the vehicle are (x f ,y f )、(x m , y m)、(x r ,y r ).

[0034] To ensure safety, the outlines of the ego vehicle and other vehicles cannot overlap. The minimum Euclidean distance between any pair of circles between the ego vehicle and any surrounding vehicle must meet the following conditions:

[0035]

[0036] Where, d i (n,n i ) is the minimum Euclidean distance between the vehicle and any pair of circles around the i-th vehicle, n and n i are the serial numbers of the circles of the self-car and the surrounding i-th car, n=1,2,3,n i =1,2,3,ε is the safety threshold, g(n,n i ) is the relative distance between the vehicle and the i-th vehicle in the circle, and its calculation formula is as follows:

[0037]

[0038] Where x n and y n are the horizontal and vertical coordinates of the nth circle of the vehicle, and The nth car around the i-th car i The horizontal and vertical coordinates of the circle, R e,n is the radius of the nth circle of the vehicle, The nth car around i i The radius of a circle.

[0039] The greater the distance between the two vehicles is above the safety threshold, the safer it is. When the safety threshold is zero, the two vehicles collide. Therefore, in order to represent extreme situations, the collision boundary of the vehicles needs to be calculated.

[0040] Step 3: Generate initial decisions based on non-cooperative non-zero-sum dynamic games.

[0041] In step three, an initial decision is generated for the vehicle based on the motion state information of the vehicle and surrounding vehicles, as well as the collision area analysis results calculated in step two. This includes steps three-one to three-three.

[0042] Step 31: Construct a forced lane-changing decision model and formulate optional game strategies.

[0043] The forced lane change decision model includes two participants, namely the ego vehicle (EV) and the target lane following vehicle (FV). The game optional strategies that meet their respective driving conditions are defined for the two participants, as shown in Table 1. EV (1,1) represents the total benefit of the ego vehicle when the ego vehicle chooses the lane change strategy and the vehicle behind the target lane chooses the avoidance strategy. U FV (1,1) represents the total benefit of the vehicle behind the target lane when the ego vehicle chooses the lane change strategy and the vehicle behind the target lane chooses the avoidance strategy.

[0044] Table 1 Strategy sets and benefits of the ego vehicle and the vehicle behind it in the target lane

[0045]

[0046] Step 32: Establish a game payoff function and calculate the total payoff of the ego vehicle and the vehicle behind it in the target lane under different strategies.

[0047] During the lane change process, collision boundary 1 between the EV and the FV is the left front of the EV and the right rear of the FV. Collision boundary 2 is the left rear of the ego vehicle and the right front of the following vehicle in the target lane. The driving safety benefit is measured based on the time difference between the EV and the FV reaching the two collision boundaries.

[0048] Specifically, when the ego vehicle chooses the lane change strategy, the driving safety benefit function of the ego vehicle and the vehicle behind it in the target lane is:

[0049]

[0050] Where, The driving safety benefit of EV when choosing to change lanes for EV, is the driving safety benefit of FV when EV chooses to change lanes, j represents the optional game strategy of FV, j = 1, 2, 3, j = 1 means the strategy of avoiding the ego vehicle, j = 2 means the strategy of not avoiding the ego vehicle, j = 3 means the strategy of keeping unchanged, t E1 and t F2 are the time when EV reaches collision boundary 1 and collision boundary 2, respectively, t F1 and t F2are the time when FV reaches collision boundary 1 and collision boundary 2 respectively, and ΔT is the safety critical time, for example, 3s. The present invention designs the speed of FV with reference to the IDM model, and calculates t for different FV strategies. F1 and t F2 , and finally the driving safety benefits of EV and FV under different strategy combinations can be obtained.

[0051] When the ego vehicle chooses not to change lanes, both the ego vehicle and the vehicle behind it in the target lane remain in their original lanes, and no collision occurs. The safety situation of both parties remains essentially unchanged, and both are driving safely. Therefore, the driving safety benefit function of the ego vehicle and the vehicle behind it in the target lane is:

[0052]

[0053] Where, The driving safety benefit of EV when EV chooses not to change lanes, Driving safety benefit of FV when EV chooses not to change lanes.

[0054] Sudden acceleration or deceleration of the vehicle will directly affect the comfort of passengers. Both EV and FV are expected to reduce the inconvenience caused by frequent speed changes. for:

[0055]

[0056] Where k1 and k2 are weight coefficients, and are the longitudinal acceleration and lateral acceleration of EV, t n is the current time, t n+m is the lane change completion time, i represents the EV’s game optional strategy, i = 1, 2, i = 1 represents the lane change strategy, and i = 2 represents the lane keeping strategy.

[0057] FV's ride comfort benefits for:

[0058]

[0059] Where, is the acceleration of the following vehicle in the target lane, determined based on the IDM model to follow the EV or LV.

[0060] When EV changes lanes, the traffic efficiency gain depends on the longitudinal speed after the lane change. When EV does not change lanes, its following target is BV, so the traffic efficiency gain of EV is for:

[0061]

[0062] Where, v LV is the speed of LV, v EV is the speed of the EV, v desired is the desired speed of the EV, is the correlation coefficient.

[0063] FV traffic efficiency benefits for:

[0064]

[0065] Where, v FV is the speed of FV, is the correlation coefficient.

[0066] After establishing the above profit functions, the weighted summation of the driving safety profit function, riding comfort profit function, and traffic efficiency profit function of the ego vehicle and the vehicle behind the target lane is performed. Finally, the total profit corresponding to the ego vehicle and the vehicle behind the target lane is obtained. The formula is as follows:

[0067]

[0068] Where U EV (i,j),U FV (i, j) are the total benefits of the ego vehicle and the vehicle behind it in the target lane, are the weighted coefficients corresponding to each part of the income.

[0069] Step 33: Conduct non-cooperative non-zero-sum dynamic game solution analysis.

[0070] By establishing a total benefit function for the ego vehicle and the vehicle behind it in the target lane, the ego vehicle and the vehicle behind it in the target lane conduct a non-cooperative non-zero-sum dynamic game based on the total benefits of different strategy combinations in the final decision to generate an initial decision.

[0071] When performing game solving analysis, the non-cooperative non-zero-sum dynamic Stackelberg game solving process is transformed into a two-level optimization problem. This process demonstrates the core characteristics of the Stackelberg game, that is, the leader (the ego car) acts according to its own priority decisions, while the follower (the car behind the target lane) reacts based on the leader's behavior. The non-cooperative non-zero-sum dynamic game process is as follows: Figure 4 As shown, the two-level optimization problem is expressed as follows:

[0072]

[0073] Where s EV * is the optimal strategy of the car, S FV * (s EV) is the optimal strategy set of the vehicle behind the target lane when the vehicle strategy is given, S EV is the optional strategy of the vehicle, S FV is the optional strategy of the vehicle behind the target lane, s FV * is the optimal strategy for the vehicle behind the target lane, U EV (S EV ,S FV ) Select S for the car EV Strategy and the target lane following vehicle selects S FV The profit of the car when using the strategy, U FV (s EV ,s FV ) is the car selection s EV Strategy and target lane following vehicle selection s FV The benefit of the vehicle behind the target lane during the strategy, U FV (s EV ,s FV * ) is the car selection s EV Strategy and target lane following vehicle selection s FV * The payoff of the vehicle behind in the target lane under the optimal strategy.

[0074] Step 4: Use a game strategy based on information continuity to optimize the initial decision.

[0075] The information continuity-based game strategy in this step introduces the concept of cumulative payoffs for participants in repeated games—the sum of historical payoffs—while also accounting for payoffs from multiple factors. This means that previous game results are considered in the repeated game model, using cumulative payoffs to mitigate fluctuations caused by momentary decision-making factors and capturing dynamic changes in information flows to enhance the adaptability and predictability of decisions.

[0076] like Figure 5 As shown, suppose the game is repeated from the initial time ts1 to the current time ts n = All the gains within T are recorded, the same interactive lane-changing decision game is repeated regularly, and the value of ts1 is adjusted in real time to achieve finite repeated game and optimize the decision model.

[0077]

[0078] Simplify the above formula to:

[0079]

[0080] In the formula, the player is the vehicle or the vehicle behind the target lane, The payoff of repeated game at time t for the player using strategy (i, j) is: is the game payoff of the game participant player adopting the (i, j) strategy at time T, δ is the time preference factor, Δt is the interval between discrete game decisions, and k is the number of historical repeated games.

[0081] Step 5: Obtain the final decision based on the game strategy of output continuity.

[0082] The game decision based on output result continuity adopted in this step is based on the condition that the game decision based on information continuity is maintained for three consecutive cycles to output the final forced lane change decision. Figure 6 As shown in the figure, the lane change decision state only lasts for one period Δt, so the cruising state in the current lane is maintained. If the interactive game decision is directly output, the ego vehicle may immediately adjust back to the original lane trajectory after just starting the lane change action. Such frequent adjustments not only have a negative impact on decision continuity but also interfere with the stable driving state of the ego vehicle.

[0083] When the interactive game decision is emergency braking, the ego-vehicle system will immediately respond and perform emergency braking to prioritize vehicle safety. When the interactive game decision is lane change, the lane change is confirmed after a continuous state information sequence to avoid decision jumps.

[0084] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for automatic driving forced lane change decision-making based on dynamic continuous game, characterized in that: The following steps are involved: Step 1: Obtaining motion state information of the vehicle and surrounding vehicles in a forced lane change scenario through on-board sensors and vehicle-to-vehicle communication. The motion state information includes the vehicle's position, velocity, and acceleration. Step 2: Use the unequal-diameter three-circle contour method to model the vehicle boundary and calculate the collision boundary between the vehicle and any surrounding vehicle; Step 3: Generate an initial decision based on a non-cooperative non-zero-sum dynamic game, which includes the following steps: Step 31: Construct a forced lane change decision model, which includes two participants: the ego vehicle and the vehicle behind in the target lane, and formulate game optional strategies for the two participants; Step 32: Establish the driving safety benefit function, ride comfort benefit function, and traffic efficiency benefit function for the ego vehicle and the vehicle behind it in the target lane, respectively. Take the weighted sum of these three benefit functions to obtain the total benefit corresponding to the ego vehicle and the vehicle behind it in the target lane. When the ego vehicle chooses the lane change strategy, the driving safety benefit function of the ego vehicle and the vehicle behind it in the target lane is: Where, For the driving safety benefit of the vehicle, is the driving safety benefit of the vehicle behind the target lane, j = 1, 2, 3, t E1 and t E2 are the time when the ego vehicle reaches the collision boundary 1 and collision boundary 2 with the vehicle behind it in the target lane, respectively, t F1 and t F2 are the time taken for the vehicle behind the target lane to reach the collision boundary 1 and collision boundary 2, respectively, and ΔT is the safety critical time; When the ego vehicle chooses not to change lanes, the driving safety benefit function of the ego vehicle and the vehicle behind it in the target lane is: Where, For the driving safety benefit of the vehicle, is the driving safety benefit of the vehicle behind in the target lane; Step 3: The ego vehicle and the vehicle behind it in the target lane perform a non-cooperative non-zero-sum dynamic game based on the total benefits of different strategy combinations to generate an initial decision; Step 4: Optimize the initial decision using a game strategy based on information continuity; Step 5: Under the condition that the strategy optimized by the game decision based on information continuity is maintained for three consecutive cycles, the final forced lane change decision is output.

2. The automatic driving forced lane change decision method based on dynamic continuous game according to claim 1, characterized in that: In the unequal diameter three-circle contour method, the contour of the circle in front of the vehicle is expanded according to the following formula: R e =R+ρve Rsafe Among them, R e is the radius of the circle in front of the vehicle, ρ is a fixed coefficient of 0.1, v is the vehicle speed, R safe is the safety factor, and R is the radius of the circle behind the vehicle and the circle in the middle of the vehicle.

3. The automatic driving forced lane change decision method based on dynamic continuous game according to claim 2, characterized in that: The collision boundary is determined according to the minimum Euclidean distance of any pair of circles between the vehicle and any surrounding vehicle being greater than or equal to a safety threshold.

4. The automatic driving forced lane change decision method based on dynamic continuous game according to claim 1, characterized in that: The value of the safety critical time ΔT is 3s.

5. The automatic driving forced lane change decision method based on dynamic continuous game according to claim 1 or 2, characterized in that: The ride comfort benefit function of the vehicle is: Where, is the ride comfort benefit of the vehicle, k1 and k2 are weight coefficients, and are the longitudinal acceleration and lateral acceleration of the vehicle, t n is the current time, t n+m is the lane change completion time, i=1,2; The ride comfort benefit function of the vehicle behind the target lane is: Where, is the ride comfort benefit of the vehicle behind the target lane, is the acceleration of the vehicle behind the target lane, which is determined by following the ego vehicle or the vehicle ahead in the target lane according to the IDM model.

6. The automatic driving forced lane change decision method based on dynamic continuous game according to claim 1 or 2, characterized in that: The traffic efficiency benefit function of the vehicle is: Where, is the traffic efficiency benefit of the vehicle, v LV is the speed of the vehicle in front of the target lane, v EV is the speed of the vehicle, v desired is the desired speed of the vehicle, is the correlation coefficient; The traffic efficiency benefit function of the vehicle behind the target lane is: Where, is the traffic efficiency gain, v FV is the speed of the vehicle behind in the target lane, is the correlation coefficient.

7. The automatic driving forced lane change decision method based on dynamic continuous game according to claim 1 or 2, characterized in that: In step 33, the non-cooperative non-zero-sum dynamic game solution process is transformed into a two-level optimization problem, which is shown in the following formula: Where s EV * is the optimal strategy of the car, S FV * (s EV ) is the optimal strategy set of the vehicle behind the target lane when the vehicle strategy is given, S EV is the optional strategy of the vehicle, S FV is the optional strategy of the vehicle behind the target lane, s FV * is the optimal strategy for the vehicle behind the target lane, U EV (s EV ,s FV ) is the car selection s EV Strategy and target lane following vehicle selection s FV The profit of the car when using the strategy, U FV (s EV ,s FV ) is the car selection s EV Strategy and target lane following vehicle selection s FV The benefit of the vehicle behind the target lane during the strategy, U FV (s EV ,s FV * ) is the car selection s EV Strategy and target lane following vehicle selection s FV * The payoff of the vehicle behind in the target lane under the optimal strategy.

8. The automatic driving forced lane change decision method based on dynamic continuous game according to claim 1 or 2, characterized in that: In step 4, the game strategy based on information continuity introduces the concept of cumulative benefits of participants in repeated games. Suppose that the repeated game is repeated from the initial time ts1 to the current time ts n = All the gains within T are recorded, the same interactive lane-changing decision game is repeated regularly, and the value of ts1 is adjusted in real time to achieve finite repeated game and optimize the decision model; Simplify the above formula to: In the formula, the player is the vehicle or the vehicle behind the target lane, The payoff of repeated game at time t for the player using strategy (i, j) is: is the game payoff of the game participant player adopting the (i, j) strategy at time T, δ is the time preference factor, Δt is the interval between discrete game decisions, and k is the number of historical repeated games.

9. The automatic driving forced lane change decision method based on dynamic continuous game according to claim 1 or 2, characterized in that: When the initial decision is emergency braking, the vehicle immediately responds and performs an emergency braking operation.

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