Autonomous vehicle adaptive pick-up system based on enhanced intelligent interaction

Abstract

The invention discloses an enhanced intelligent interaction-based self-adaptive pick-up and drop-off system for an autonomous vehicle. The system comprises a passenger intelligent interaction control module, a multi-modal large model optimization module, a real-time path planning and dynamic adjustment module, a passenger feedback and personalized recommendation module and an intelligent accident response and safety module. The passenger is allowed to autonomously regulate and control the pick-up point and adjust the path in real time through the mobile device, the pick-up scheme is intelligently optimized according to traffic conditions, road changes and emergencies under the support of the multi-mode large model, the method is particularly suitable for scenes where the passengers have flexible requirements for the pick-up point, and the method is suitable for popularization and application. The pick-up efficiency and the passenger experience can be improved in a dynamically changing environment.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to an adaptive pick-up and drop-off system for autonomous driving vehicles based on enhanced intelligent interaction. Background Art

[0002] As a strategic direction for the development of the global automotive industry, autonomous driving technology has become one of the key technologies for solving traffic problems. With the development and maturity of new-generation digital technologies such as artificial intelligence, the Internet of Things, and 5G mobile communications, the level of automotive automation and intelligence has continued to improve, and the market's acceptance of autonomous driving has also continued to increase. The development level of autonomous driving technology is directly related to the international competitiveness of the automotive industry of various countries and the global industrial division of labor. Therefore, major countries in the world attach great importance to the development of autonomous driving and are actively exploring traffic laws and regulations, regulatory policies, and other aspects.

[0003] The trend of integration of augmented intelligence (IA) and artificial intelligence (AI) is becoming increasingly obvious. Augmented intelligence focuses on the auxiliary role of artificial intelligence, emphasizing that the purpose of cognitive technology is to improve human intelligence and ability, while artificial intelligence aims to create systems that do not require human operation. At present, most cognitive intelligence products and project solutions are a combination of the two technologies. As the boundaries between the two become more and more blurred, artificial intelligence and augmented intelligence are also moving towards integration, and a new era of augmented intelligence and human-machine collaboration is coming.

[0004] Current autonomous vehicle pick-up and drop-off systems usually rely on preset pick-up and drop-off points or fixed route planning, which cannot fully cope with changes in dynamic environments, such as traffic congestion, road closures or emergencies. For passengers, fixed pick-up and drop-off points and inflexible routes often lead to a poor pick-up and drop-off experience. Summary of the invention

[0005] The purpose of the present invention is to provide an adaptive pick-up and drop-off system for autonomous driving vehicles based on enhanced intelligent interaction, so as to solve the above-mentioned problems existing in the prior art.

[0006] The specific application is as follows:

[0007] The self-adaptive pick-up and drop-off system for autonomous vehicles based on enhanced intelligent interaction includes a passenger intelligent interaction control module, a multimodal large model optimization module, a real-time path planning and dynamic adjustment module, a passenger feedback and personalized recommendation module, and an intelligent accident response and safety module;

[0008] The passenger intelligent interactive control module is used to provide a real-time interactive interface between passengers and vehicles, so that passengers can independently select, adjust or change the pick-up point through mobile devices and control the vehicle's driving path in real time;

[0009] The multimodal large model optimization module analyzes the behavior pattern of each passenger in the group through the multimodal large model, combines the current environmental data and historical behavior data, and predicts the future action path and time schedule of each passenger in the group;

[0010] The real-time path planning and dynamic adjustment module is used to generate the real-time driving path of the vehicle and allow passengers to dynamically adjust the path during driving;

[0011] The passenger feedback and personalized recommendation module generates personalized pick-up and drop-off point recommendations and optimization plans based on the passenger's historical behavior, preferences and feedback;

[0012] The intelligent accident response and safety module is used to provide intelligent response measures and safety assurance for emergencies;

[0013] The passenger intelligent interaction control module, the multimodal large model optimization module, the real-time path planning and dynamic adjustment module, the passenger feedback and personalized recommendation module and the intelligent accident response and safety module are interconnected.

[0014] Further, the passenger intelligent interaction control module includes a pick-up point selection submodule, a route adjustment submodule and a preference setting submodule;

[0015] The pick-up point selection submodule is used for passengers to select from a plurality of predefined pick-up points or manually input a new pick-up point;

[0016] The route adjustment submodule is used for passengers to view the vehicle's route in real time and actively adjust the route when road congestion or accidents occur;

[0017] The preference setting submodule is used to set the passenger's personalized pick-up and drop-off preferences through a decision tree model and generate suggestions, wherein the personalized pick-up and drop-off preferences at least include selecting a quieter pick-up and drop-off point, being close to a building entrance, and avoiding crowded areas;

[0018] The specific implementation process of the pick-up point selection submodule is as follows:

[0019] The deep learning model is used to parse the pick-up and drop-off points entered by the passenger through voice or text input. The specific process is as follows:

[0020] Voice input, using a Wave2Vec-based speech recognition model to convert speech into text;

[0021] Text parsing: Use the BERT model to perform semantic analysis and extract the location, time, and route requirements of the ride;

[0022] The specific implementation process of the path adjustment submodule is as follows:

[0023] Route planning is performed based on the graph search A* algorithm. If the passenger changes the pick-up point or route, the optimal route is dynamically recalculated. The formula is:

[0024] f(n)=g(n)+h(n),

[0025] Among them, f(n) represents the total cost, g(n) represents the actual cost from the starting point to the current node, and h(n) represents the estimated cost from the current node to the target;

[0026] The specific implementation process of the preference setting submodule is as follows:

[0027] Feature fusion, inputting the passenger's immediate needs and long-term preferences as features into a decision tree model and training the model, wherein the immediate needs are current specific conditions, including at least weather, time and location, and the long-term preferences represent passenger preference statistics based on historical data;

[0028] Weight setting: set different weights for different features, and set the weight of long-term preferences higher than the weight of immediate needs;

[0029] Dynamic adjustment: Dynamically adjust weight settings based on real-time feedback during decision tree model training;

[0030] Model evaluation and iteration: regularly use new data evaluation models to evaluate the decision tree model, and update the parameters of the decision tree model according to the evaluation results;

[0031] The model is deployed and used to prioritize passengers' different preferences using the trained decision tree model, and to make personalized pick-up and drop-off demand recommendations based on passenger historical records.

[0032] Furthermore, the multimodal large model optimization module includes a multimodal input processing submodule, a traffic and environment perception submodule, and an intelligent optimization engine submodule;

[0033] The multimodal input processing submodule is used to analyze the passenger's voice or text input through natural language processing technology to generate text input data; confirm the environmental image uploaded by the passenger through image analysis technology to generate image input data; perform data fusion processing through the conditional variational autoencoding CVAE model to generate multimodal fusion data;

[0034] The traffic and environment perception submodule is used to obtain real-time traffic and environment data from traffic sensors, map APIs, and weather APIs. The real-time traffic and environment data is used to optimize the vehicle's driving path and pick-up points;

[0035] The intelligent optimization engine submodule is used to predict and analyze the real-time traffic and environmental data through a multi-modal large model, and intelligently adjust the passenger pick-up and drop-off plan.

[0036] Furthermore, the specific implementation process of data fusion processing through the conditional variational auto-encoding CVAE model in the multimodal input processing submodule is as follows:

[0037] Speech processing, using Wave2Vec to convert passengers’ speech into text data;

[0038] Image processing: For the environmental images uploaded by passengers, DeepLabv3+ image semantic segmentation network is used to extract features, and the attention mechanism is integrated to enhance the recognition of objects in the environmental images to generate image semantic segmentation data;

[0039] Text parsing: Use the GPT-4 model to perform semantic analysis on the text input of passengers and generate text semantic parsing data;

[0040] The conditional variational autoencoding (CVAE) model embeds the text data, the image semantic segmentation data, and the text semantic parsing data into the same vector space, and realizes multimodal fusion by maximizing the similarity between related modalities.

[0041] The specific implementation process of the intelligent optimization engine submodule for predicting and analyzing the real-time traffic and environmental data through a multi-modal large model and intelligently adjusting the passenger pick-up and drop-off plan is as follows:

[0042] The multimodal large model includes a time coding model, a space coding model, two conditional variational autoencoding CVAE models, a fusion decoding model and a semantic BEV modal prediction model. The time coding model takes the historical behavior data of the target passenger to be predicted as input and generates time coding. The space coding model takes the multimodal fusion data as input and generates space coding. The time coding model and the space coding model will generate time-space features. The time coding model adopts a bidirectional long short-term memory LSTM network, and the space coding model adopts a DenseNet network. The two conditional variational autoencoding CVAE models include a first CVAE model and a second CVAE model; the output of the time coding model is connected to the input of the first CVAE model; the output of the space coding model is connected to the input of the second CVAE model; the fusion decoding model converts the connected time-space features into a prediction trajectory with confidence; the semantic BEV modal prediction model is used to optimize the confidence of the prediction trajectory;

[0043] The real-time prediction process is:

[0044] Decomposing the entire prediction task into two sub-models: a modal prediction model TPM based on modal distribution and a trajectory prediction model MPM based on modal trajectory distribution, wherein both sub-models are based on the multi-modal large model;

[0045] Given historical passenger behavior data and real-time traffic and environmental data, TPM performs probabilistic inference on predicted trajectories for all modalities;

[0046] The trajectory prediction model MPM of the modal trajectory distribution is used to solve the comprehensive probability of different modes and compare it with the preset probability threshold to determine whether the comprehensive probability is greater than the preset probability threshold. If so, the passenger pick-up and drop-off plan is adjusted, and the semantic BEV modal prediction model is used to optimize the confidence of each modal trajectory prediction.

[0047] Further, the real-time path planning and dynamic adjustment module includes a real-time path planning submodule and a dynamic adjustment and optimization submodule;

[0048] The real-time route planning submodule generates an optimal driving route based on the pick-up point currently selected by the passenger and the real-time traffic conditions, and recalculates the route at each change, including changes to the pick-up point and the real-time traffic conditions;

[0049] The dynamic adjustment and optimization submodule is used to generate an alternative path when an emergency occurs to the vehicle, and adjust the alternative path according to the feedback from the passengers to generate a final driving path.

[0050] Furthermore, the specific implementation process of the real-time path planning submodule is as follows:

[0051] Combine multi-sensor data and use SLAM technology for environmental mapping and self-positioning to achieve dynamic path adjustment;

[0052] Based on the pick-up and drop-off points selected by passengers and real-time traffic data, the A* algorithm is used to generate the optimal path. The path planning formula uses the heuristic function:

[0053]

[0054] Among them, H(n) represents the estimated cost from the current node n to the target node, (x1, y1) represents the coordinates of the current node, and (x2, y2) represents the coordinates of the target node;

[0055] The specific process of the dynamic adjustment and optimization submodule is as follows:

[0056] When an emergency event is detected, a new path plan is generated using a genetic algorithm, wherein the genetic algorithm generates a path population through crossover, mutation, and selection operations, scores the adaptability of each path, and selects the optimal path;

[0057] The alternative path generation formula is:

[0058] C(x,y)=min[C(x-1,y),C(x,y-1)]+w(x,y),

[0059] Among them, C(x,y) represents the minimum cost from the starting point to the current node (x,y), and w(x,y) represents the weight of the current node.

[0060] Further, the passenger feedback and personalized recommendation module includes a personalized recommendation submodule and a real-time feedback submodule;

[0061] The personalized recommendation submodule is used to preferentially recommend the pick-up and drop-off points and routes preferred by the passenger based on the passenger's historical data, wherein the historical data includes the pick-up and drop-off points used previously and the routes liked previously;

[0062] The real-time feedback submodule is used to provide passengers with the vehicle's real-time location, estimated arrival time, and route suggestions through smartphones, and to adjust the pick-up and drop-off plan in real time based on the passenger's feedback;

[0063] The specific implementation process of the personalized recommendation submodule is as follows:

[0064] Use collaborative filtering technology to analyze passengers' historical pick-up and drop-off data, and use the matrix decomposition model to achieve personalized recommendations. The formula is:

[0065] R=P*Q T ,

[0066] Among them, R represents the rating matrix of the pick-up point, P and Q are the feature matrices of passengers and pick-up points respectively. By optimizing P and Q, the passenger's rating of the recommended pick-up point is predicted.

[0067] Further, the intelligent accident response and safety module includes an accident response submodule and a safety priority mechanism submodule;

[0068] The accident response submodule is used to automatically generate an alternative route and notify passengers to adjust the route when an accident or road closure is detected;

[0069] The safety priority mechanism submodule prioritizes the safest pick-up and drop-off plan based on real-time traffic and environmental data. Furthermore, the specific implementation process of the accident response submodule is as follows:

[0070] If an accident or road closure is detected, the Bayesian network is used to calculate the probabilities of different response plans and select the optimal alternative path. The formula is:

[0071]

[0072] Among them, P(H|E) represents the probability that hypothesis H is true when it is known that event E has occurred, P(E|H) represents the probability that event E occurs under hypothesis H, and P(H) and P(E) represent the prior probabilities of hypothesis H and time E;

[0073] The specific implementation process of the safety priority mechanism submodule is as follows:

[0074] The Markov decision process MDP is used to calculate the risk of each state according to the environmental data and select the solution with the highest security. The Markov decision formula is:

[0075] V(s)=max a [R(s,a)+b∑ s' P(s'|s,a)V(s'))],

[0076] Among them, V(s) represents the value of state s, R(s,a) represents the immediate reward of executing action a in state s, b represents the discount factor, and P(s'|s,a) represents the transition probability from state s to state s'.

[0077] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects:

[0078] The embodiment of the present invention provides a passenger intelligent interactive control module for providing a real-time interactive interface between passengers and vehicles. Passengers can independently select, adjust or change pick-up points through mobile devices and control the driving path of the vehicle in real time; the multimodal large model optimization module analyzes the behavior pattern of each passenger in the group through the multimodal large model, and predicts the future action path and time arrangement of each passenger in the group in combination with the current environmental data and historical behavior data; the real-time path planning and dynamic adjustment module is used to generate the real-time driving path of the vehicle and allow passengers to dynamically adjust the path during driving; the passenger feedback and personalized recommendation module generates personalized pick-up point recommendations and optimization plans based on the historical behavior, preferences and feedback of the passengers; the intelligent accident response and safety module is used to provide intelligent response measures and safety guarantees for emergencies; the present invention allows passengers to autonomously adjust the pick-up points through mobile devices, adjust the paths in real time, and intelligently optimize the pick-up plans according to traffic conditions, road changes and emergencies with the support of the multimodal large model. The present invention is particularly suitable for scenarios where passengers have flexible requirements for pick-up points, and can improve pick-up efficiency and passenger experience in a dynamically changing environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 is a system architecture diagram of an autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction provided by an embodiment of the present invention;

[0080] Figure 2It is a passenger intelligent interaction control module diagram of an adaptive pick-up and drop-off system for an autonomous driving vehicle based on enhanced intelligent interaction provided by an embodiment of the present invention;

[0081] Figure 3 It is a multi-modal large model optimization module diagram of an autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction provided by an embodiment of the present invention;

[0082] Figure 4 It is a real-time path planning and dynamic adjustment module diagram of an adaptive pick-up and drop-off system for an autonomous driving vehicle based on enhanced intelligent interaction provided by an embodiment of the present invention;

[0083] Figure 5 It is a passenger feedback and personalized recommendation module diagram of an adaptive pick-up and drop-off system for an autonomous driving vehicle based on enhanced intelligent interaction provided by an embodiment of the present invention;

[0084] Figure 6 It is a diagram of an intelligent accident response and safety module of an adaptive pick-up and drop-off system for an autonomous driving vehicle based on enhanced intelligent interaction provided by an embodiment of the present invention;

[0085] Figure 7 It is a schematic diagram of the A* algorithm flow of the autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction provided by an embodiment of the present invention;

[0086] Figure 8 is a schematic diagram of the SENet structure of an adaptive pick-up and drop-off system for autonomous driving vehicles based on enhanced intelligent interaction provided by an embodiment of the present invention;

[0087] Fig. 9 It is a schematic diagram of the CBAM structure of the autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0088] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0089] Example 1

[0090] The embodiment of the present invention provides an adaptive pick-up and drop-off system for autonomous driving vehicles based on enhanced intelligent interaction, such as Figure 1 As shown, it includes passenger intelligent interaction control module, multimodal large model optimization module, real-time path planning and dynamic adjustment module, passenger feedback and personalized recommendation module and intelligent accident response and safety module;

[0091] The passenger intelligent interactive control module is used to provide a real-time interactive interface between passengers and vehicles, so that passengers can independently select, adjust or change the pick-up point through mobile devices and control the vehicle's driving path in real time;

[0092] The multimodal large model optimization module analyzes the behavior pattern of each passenger in the group through the multimodal large model, combines the current environmental data and historical behavior data, and predicts the future action path and time schedule of each passenger in the group;

[0093] The real-time path planning and dynamic adjustment module is used to generate the real-time driving path of the vehicle and allow passengers to dynamically adjust the path during driving;

[0094] The passenger feedback and personalized recommendation module generates personalized pick-up and drop-off point recommendations and optimization plans based on the passenger's historical behavior, preferences and feedback;

[0095] The intelligent accident response and safety module is used to provide intelligent response measures and safety assurance for emergencies;

[0096] The passenger intelligent interaction control module, the multimodal large model optimization module, the real-time path planning and dynamic adjustment module, the passenger feedback and personalized recommendation module and the intelligent accident response and safety module are interconnected.

[0097] Furthermore, the passenger intelligent interaction control module includes a pick-up point selection submodule, a route adjustment submodule and a preference setting submodule, such as Figure 2 As shown;

[0098] The pick-up point selection submodule is used for passengers to select from a plurality of predefined pick-up points or manually input a new pick-up point;

[0099] The route adjustment submodule is used for passengers to view the vehicle's route in real time and actively adjust the route when road congestion or accidents occur;

[0100] The preference setting submodule is used to set the passenger's personalized pick-up and drop-off preferences through a decision tree model and generate suggestions, wherein the personalized pick-up and drop-off preferences at least include selecting a quieter pick-up and drop-off point, being close to a building entrance, and avoiding crowded areas;

[0101] The specific implementation process of the pick-up point selection submodule is as follows:

[0102] The deep learning model is used to parse the pick-up and drop-off points entered by the passenger through voice or text input. The specific process is as follows:

[0103] Voice input, using a Wave2Vec-based speech recognition model to convert speech into text;

[0104] Text parsing: Use the BERT model to perform semantic analysis and extract the location, time, and route requirements of the ride;

[0105] The specific implementation process of the path adjustment submodule is as follows:

[0106] Route planning is performed based on the graph search A* algorithm. If the passenger changes the pick-up point or route, the optimal route is dynamically recalculated. The formula is:

[0107] f(n)=g(n)+h(n),

[0108] Among them, f(n) represents the total cost, g(n) represents the actual cost from the starting point to the current node, and h(n) represents the estimated cost from the current node to the target;

[0109] The specific implementation process of the preference setting submodule is as follows:

[0110] Feature fusion, inputting the passenger's immediate needs and long-term preferences as features into a decision tree model and training the model, wherein the immediate needs are current specific conditions, including at least weather, time and location, and the long-term preferences represent passenger preference statistics based on historical data;

[0111] Weight setting: set different weights for different features, and set the weight of long-term preferences higher than the weight of immediate needs;

[0112] Dynamic adjustment: Dynamically adjust weight settings based on real-time feedback during decision tree model training;

[0113] Model evaluation and iteration: regularly use new data evaluation models to evaluate the decision tree model, and update the parameters of the decision tree model according to the evaluation results;

[0114] The model is deployed and used to prioritize passengers' different preferences using the trained decision tree model, and to make personalized pick-up and drop-off demand recommendations based on passenger historical records.

[0115] Furthermore, the multimodal large model optimization module includes a multimodal input processing submodule, a traffic and environment perception submodule and an intelligent optimization engine submodule, such as Figure 3 As shown;

[0116] Specifically, the process of interaction between the passenger intelligent interaction control module and the multimodal large model optimization module is as follows:

[0117] Interaction Description:

[0118] Input: The passenger intelligent interaction control module receives instructions from passengers, such as pick-up point selection, route adjustment, preference setting, etc.

[0119] Output: These instructions are used as input to the multimodal large model optimization module to convey passengers’ needs, such as text, voice, images, etc.

[0120] Algorithm model: Passenger instructions are parsed through a natural language processing model, including but not limited to BERT, and then integrated with real-time traffic environment data to generate appropriate pick-up points and route optimization solutions through CLIP or multimodal Transformer models.

[0121] The multimodal input processing submodule is used to analyze the passenger's voice or text input through natural language processing technology to generate text input data; confirm the environmental image uploaded by the passenger through image analysis technology to generate image input data; perform data fusion processing through the conditional variational autoencoding CVAE model to generate multimodal fusion data;

[0122] The traffic and environment perception submodule is used to obtain real-time traffic and environment data from traffic sensors, map APIs, and weather APIs. The real-time traffic and environment data is used to optimize the vehicle's driving path and pick-up points;

[0123] The intelligent optimization engine submodule is used to predict and analyze the real-time traffic and environmental data through a multi-modal large model, and intelligently adjust the passenger pick-up and drop-off plan.

[0124] Furthermore, the specific implementation process of data fusion processing through the conditional variational auto-encoding CVAE model in the multimodal input processing submodule is as follows:

[0125] Speech processing, using Wave2Vec to convert passengers’ speech into text data;

[0126] Image processing: For the environmental images uploaded by passengers, DeepLabv3+ image semantic segmentation network is used to extract features, and the attention mechanism is integrated to enhance the recognition of objects in the environmental images to generate image semantic segmentation data;

[0127] It should be noted that the fusion attention mechanism adopts the channel attention mechanism SENet architecture and the spatial attention mechanism CBAM architecture;

[0128] The specific calculation process of SENet includes three steps: compression, expansion, and weighting. Figure 8 As shown:

[0129] a1. Through compression operation F sq The input feature map H∈R H*W*C Compressed into a one-dimensional vector z∈R with C channels through the global average pooling layer 1*1*C , F sq The calculation formula is:

[0130]

[0131] Among them, u c ∈RH*W Represents a pixel point on a feature channel in the feature map, C represents the number of channels, H represents the number of rows in the feature map, and W represents the number of columns in the feature map;

[0132] a2. Then through the expansion operation F ex First, the obtained one-dimensional vector z is mapped to a one-dimensional vector with a channel number C′ through a fully connected layer, and then it is expanded back to a one-dimensional channel weight vector s∈R with a channel number C through a fully connected layer. 1*1*C , F ex The calculation formula is:

[0133]

[0134] Where C' = C / r, r represents the scaling factor. σ represents the Sigmoid activation function, δ represents the Relu activation function, and W2∈R C ' *C represents the fully connected layer, W1∈R C*C ' represents a fully connected layer;

[0135] a3.Finally, the weighted operation F scale Multiplying the one-dimensional channel weight vector s with the original input feature map H in the channel dimension will give the channel-weighted output feature map H'∈R H*W*C , F scale The calculation formula is:

[0136] H'=F scale (u c ,s c )=u c .s c ,

[0137] Among them, s c ∈R C Represents the weight of a channel;

[0138] SENet focuses on channels with greater feature contribution, which is an effective method to filter redundant feature channel information. It performs feature filtering on environmental images uploaded by passengers, filters invalid redundant features, and improves feature extraction efficiency.

[0139] The specific calculation process of CBAM includes three steps: compression, fusion, and weighting. Fig. 9 As shown:

[0140] b1. Input feature map H∈R H*W*C Perform the maximum pooling operation F on the channel dimension respectively max and average pooling operation F avg Get the two-dimensional vector z1∈R H*W*1 and z2∈R H*W*1 , F maxand F avg The calculation formula is:

[0141] z1=F max (v c )=max(v c ),

[0142]

[0143] Among them, v c ∈R C Represents a pixel point at a certain pixel position in the feature map;

[0144] b2. Fuse z1 and z2 cat , that is, stacking and fusion are performed on the channel dimension to obtain z m ∈R H*W*2 , and then z m Use 7×7 convolution to reduce the dimension F conv Get the spatial two-dimensional weight vector s∈R H*W*1 , F max and F avg The calculation formula is:

[0145] z m =F cat (z1,z2)=cat(z1,z1),

[0146] s=F conv (z m )=f 7×7 (z m ),

[0147] Among them, f 7×7 Indicates that the convolution kernel is 7;

[0148] b3. Through weighted operation F scale Multiply the spatial two-dimensional weight s with the corresponding position of the original input feature map H in the spatial dimension to obtain the output feature map H'∈R after spatial dimension weighting H*W*C , fusion F of z1 and z2 cat , that is, stacking and fusion are performed on the channel dimension to obtain z m ∈R H*W*2 , F scale The calculation formula is:

[0149] H'=F scale (s c ,v c )=s c .v c ,

[0150] Among them, s c ∈R H*WRepresents the weight of a pixel position on the feature map;

[0151] The CBAM attention mechanism is a good combination of the channel attention mechanism and the spatial attention mechanism to enhance the feature extraction of environmental images.

[0152] Text parsing: Use the GPT-4 model to perform semantic analysis on the text input of passengers and generate text semantic parsing data;

[0153] The conditional variational autoencoding (CVAE) model embeds the text data, the image semantic segmentation data, and the text semantic parsing data into the same vector space, and realizes multimodal fusion by maximizing the similarity between related modalities.

[0154] The specific implementation process of the intelligent optimization engine submodule for predicting and analyzing the real-time traffic and environmental data through a multi-modal large model and intelligently adjusting the passenger pick-up and drop-off plan is as follows:

[0155] The multimodal large model includes a time coding model, a space coding model, two conditional variational autoencoding CVAE models, a fusion decoding model and a semantic BEV modal prediction model. The time coding model takes the historical behavior data of the target passenger to be predicted as input and generates time coding. The space coding model takes the multimodal fusion data as input and generates space coding. The time coding model and the space coding model will generate time-space features. The time coding model adopts a bidirectional long short-term memory LSTM network, and the space coding model adopts a DenseNet network. The two conditional variational autoencoding CVAE models include a first CVAE model and a second CVAE model; the output of the time coding model is connected to the input of the first CVAE model; the output of the space coding model is connected to the input of the second CVAE model; the fusion decoding model converts the connected time-space features into a prediction trajectory with confidence; the semantic BEV modal prediction model is used to optimize the confidence of the prediction trajectory;

[0156] The real-time prediction process is:

[0157] Decomposing the entire prediction task into two sub-models: a modal prediction model TPM based on modal distribution and a trajectory prediction model MPM based on modal trajectory distribution, wherein both sub-models are based on the multi-modal large model;

[0158] Given historical passenger behavior data and real-time traffic and environmental data, TPM performs probabilistic inference on predicted trajectories for all modalities;

[0159] The trajectory prediction model MPM of the modal trajectory distribution is used to solve the comprehensive probability of different modes and compare it with the preset probability threshold to determine whether the comprehensive probability is greater than the preset probability threshold. If so, the passenger pick-up and drop-off plan is adjusted, and the semantic BEV modal prediction model is used to optimize the confidence of each modal trajectory prediction.

[0160] Furthermore, the real-time path planning and dynamic adjustment module includes a real-time path planning submodule and a dynamic adjustment and optimization submodule, such as Figure 4 As shown;

[0161] The real-time route planning submodule generates an optimal driving route based on the pick-up point currently selected by the passenger and the real-time traffic conditions, and recalculates the route at each change, including changes to the pick-up point and the real-time traffic conditions;

[0162] The dynamic adjustment and optimization submodule is used to generate an alternative path when an emergency occurs to the vehicle, and adjust the alternative path according to the feedback from the passengers to generate a final driving path.

[0163] Furthermore, the specific implementation process of the real-time path planning submodule is as follows:

[0164] Combine multi-sensor data and use SLAM technology for environmental mapping and self-positioning to achieve dynamic path adjustment;

[0165] Based on the pick-up and drop-off points selected by passengers and real-time traffic data, the A* algorithm is used to generate the optimal path. The path planning formula uses the heuristic function:

[0166]

[0167] Among them, H(n) represents the estimated cost from the current node n to the target node, (x1, y1) represents the coordinates of the current node, and (x2, y2) represents the coordinates of the target node;

[0168] Specifically, in the A* algorithm, there are two important sets: Open list and Closed list. Points that have not been detected during the path planning process are stored in the Open list, and points that have been detected (or passed) are stored in the Closed list. Points in the Open list may not be passed in the future planned path. First, put the starting point A into the Open list, and check the reachable points around the starting point. This embodiment is set as adjacent points, put these points into the Open list, set the starting point as the parent node of these points, and move the starting point from the Open list to the Closed list. Then, the algorithm will select a point B in the Open list with the smallest F(n) value with the starting point, and use point B as the current processing node, and repeat the above operations until the destination node is added to the Open list. The specific process is as follows: Figure 7 As shown;

[0169] In the present invention, for a vehicle, the center position coordinates (x, y) of the path point are used. According to such rules, the networkx toolkit is used to establish the traffic road into a topological graph structure, so that the traffic road is abstracted into the form of a topological graph. The A* algorithm is used to search for multiple path point sequences from the current point to the global target, and the sequences are arranged according to the distance, and the shortest path is taken as the target path.

[0170] The specific process of the dynamic adjustment and optimization submodule is as follows:

[0171] When an emergency event is detected, a new path plan is generated using a genetic algorithm, wherein the genetic algorithm generates a path population through crossover, mutation, and selection operations, scores the adaptability of each path, and selects the optimal path;

[0172] The alternative path generation formula is:

[0173] C(x,y)=min[C(x-1,y),C(x,y-1)]+w(x,y),

[0174] Among them, C(x,y) represents the minimum cost from the starting point to the current node (x,y), and w(x,y) represents the weight of the current node.

[0175] Furthermore, the passenger feedback and personalized recommendation module includes a personalized recommendation submodule and a real-time feedback submodule, such as Figure 5 As shown;

[0176] Specifically, the passenger feedback and personalized recommendation module interacts with the multimodal large model optimization module in the following process:

[0177] Interaction Description:

[0178] Input: The passenger feedback module obtains information from passengers’ historical behaviors, preferences, and real-time feedback;

[0179] Output: These feedback information are passed to the multimodal large model optimization module for personalized recommendation of pick-up points and routes;

[0180] Algorithm model: Generate personalized pick-up and drop-off point recommendations for passengers through collaborative filtering and matrix decomposition technology.

[0181] The personalized recommendation submodule is used to preferentially recommend the pick-up and drop-off points and routes preferred by the passenger based on the passenger's historical data, wherein the historical data includes the pick-up and drop-off points used previously and the routes liked previously;

[0182] The real-time feedback submodule is used to provide passengers with the vehicle's real-time location, estimated arrival time, and route suggestions through smartphones, and to adjust the pick-up and drop-off plan in real time based on the passenger's feedback;

[0183] The specific implementation process of the personalized recommendation submodule is as follows:

[0184] Use collaborative filtering technology to analyze passengers' historical pick-up and drop-off data, and use the matrix decomposition model to achieve personalized recommendations. The formula is:

[0185] R=P*Q T ,

[0186] Among them, R represents the rating matrix of the pick-up point, P and Q are the feature matrices of passengers and pick-up points respectively. By optimizing P and Q, the passenger's rating of the recommended pick-up point is predicted.

[0187] Furthermore, the intelligent accident response and safety module includes an accident response submodule and a safety priority mechanism submodule, such as Figure 6 As shown;

[0188] Specifically, the real-time path planning and dynamic adjustment module interacts with the intelligent accident response and safety module as follows:

[0189] Interaction Description:

[0190] Input: The real-time path planning module senses emergencies through vehicle sensors and external data, such as road conditions and accident data;

[0191] Output: The real-time path planning module generates alternative paths and notifies the intelligent accident response module;

[0192] Algorithm model: When a road closure or traffic accident is detected, the system uses Bayesian networks and Markov decision processes to calculate a safe path and the optimal response plan.

[0193] The accident response submodule is used to automatically generate an alternative route and notify passengers to adjust the route when an accident or road closure is detected;

[0194] The safety priority mechanism submodule prioritizes the safest pick-up and drop-off plan based on real-time traffic and environmental data. Furthermore, the specific implementation process of the accident response submodule is as follows:

[0195] If an accident or road closure is detected, the Bayesian network is used to calculate the probabilities of different response plans and select the optimal alternative path. The formula is:

[0196]

[0197] Among them, P(H|E) represents the probability that hypothesis H is true when it is known that event E has occurred, P(E|H) represents the probability that event E occurs under hypothesis H, and P(H) and P(E) represent the prior probabilities of hypothesis H and time E;

[0198] The specific implementation process of the safety priority mechanism submodule is as follows:

[0199] The Markov decision process MDP is used to calculate the risk of each state according to the environmental data and select the solution with the highest security. The Markov decision formula is:

[0200] V(s)=max a [R(s,a)+b∑ s' P(s'|s,a)V(s'))],

[0201] Among them, V(s) represents the value of state s, R(s,a) represents the immediate reward of executing action a in state s, b represents the discount factor, and P(s'|s,a) represents the transition probability from state s to state s'.

[0202] Example 2: Real-time route adjustment of passengers in busy streets

[0203] 1. Background description: The passenger is in a busy urban area. The autonomous vehicle encounters a traffic jam during the pick-up. The passenger hopes to adjust the pick-up plan by himself through the smartphone.

[0204] 2. Passenger operation:

[0205] Passengers use the smartphone app to view the vehicle's real-time route and find that the road ahead is congested due to an accident. Passengers manually select an alternative pick-up point in the app and confirm the updated route;

[0206] 3. System optimization: The system recalculates the new pick-up and drop-off routes through a multimodal large model and generates the optimal driving plan. At the same time, it pushes the updated vehicle arrival time to passengers through the mobile application;

[0207] 4. Dynamic feedback: The vehicle arrives at the alternative pick-up point specified by the passenger based on the new route, ensuring a smooth and error-free pick-up process.

[0208] Example 3: Intelligent path switching in emergency situations

[0209] 1. Background description: A passenger was on his way to the conference center when the original route was suddenly blocked. The system automatically detected and generated an alternative plan;

[0210] 2. System operation:

[0211] The system detects that the road ahead is closed and immediately generates an alternative route, notifying the passenger to confirm the new pick-up point through the app. The system analyzes the current traffic conditions through a multimodal large model and selects the optimal pick-up point to avoid congested areas;

[0212] 3. Passenger confirmation: The passenger confirms the new pick-up plan through the intelligent interactive interface, and the vehicle goes to the new pick-up point according to the adjusted route to ensure that the passenger arrives on time.

[0213] Example 4: Personalized Pick-up Point Preference Management

[0214] 1. Background description: A passenger is used to getting on and off the bus at the entrance of a quiet neighborhood. The system learns the passenger's pick-up and drop-off preferences and provides personalized suggestions in future pick-up and drop-off tasks;

[0215] 2. System operation:

[0216] The system analyzes the passenger's historical pick-up and drop-off point records and prioritizes pick-up and drop-off points that are quieter and closer to the building entrance. Passengers can view the system's recommended pick-up and drop-off options through the app and confirm or adjust the final pick-up and drop-off point;

[0217] 3. Dynamic optimization: Based on traffic and environmental changes, the system dynamically optimizes the route during the pick-up process to ensure that the passenger's pick-up experience meets their preferences.

[0218] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific languages ​​is for disclosing the best mode of the present invention.

[0219] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0220] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0221] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0222] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0223] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the apparatus according to an embodiment of the present invention. The present invention may also be implemented as a device or device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may have the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

Claims

1. An adaptive pick-up and drop-off system for autonomous driving vehicles based on enhanced intelligent interaction, characterized in that: It includes passenger intelligent interactive control module, multimodal large model optimization module, real-time path planning and dynamic adjustment module, passenger feedback and personalized recommendation module and intelligent accident response and safety module; The passenger intelligent interactive control module is used to provide a real-time interactive interface between passengers and vehicles, so that passengers can independently select, adjust or change the pick-up point through mobile devices and control the vehicle's driving path in real time; The multimodal large model optimization module analyzes the behavior pattern of each passenger in the group through the multimodal large model, combines the current environmental data and historical behavior data, and predicts the future action path and time schedule of each passenger in the group; The real-time path planning and dynamic adjustment module is used to generate the real-time driving path of the vehicle and allow passengers to dynamically adjust the path during driving; The passenger feedback and personalized recommendation module generates personalized pick-up and drop-off point recommendations and optimization plans based on the passenger's historical behavior, preferences and feedback; The intelligent accident response and safety module is used to provide intelligent response measures and safety assurance for emergencies; The passenger intelligent interaction control module, the multimodal large model optimization module, the real-time path planning and dynamic adjustment module, the passenger feedback and personalized recommendation module and the intelligent accident response and safety module are interconnected.

2. The autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction according to claim 1 is characterized in that: The passenger intelligent interaction control module includes a pick-up point selection submodule, a route adjustment submodule and a preference setting submodule; The pick-up point selection submodule is used for passengers to select from a plurality of predefined pick-up points or manually input a new pick-up point; The route adjustment submodule is used for passengers to view the vehicle's route in real time and actively adjust the route when road congestion or accidents occur; The preference setting submodule is used to set the passenger's personalized pick-up and drop-off preferences through a decision tree model and generate suggestions, wherein the personalized pick-up and drop-off preferences at least include selecting a quieter pick-up and drop-off point, being close to a building entrance, and avoiding crowded areas; The specific implementation process of the pick-up point selection submodule is as follows: The deep learning model is used to parse the pick-up and drop-off points entered by the passenger through voice or text input. The specific process is as follows: Voice input, using a Wave2Vec-based speech recognition model to convert speech into text; Text parsing: Use the BERT model to perform semantic analysis and extract the location, time, and route requirements of the ride; The specific implementation process of the path adjustment submodule is as follows: Route planning is performed based on the graph search A* algorithm. If the passenger changes the pick-up point or route, the optimal route is dynamically recalculated. The formula is: f(n)=g(n)+h(n), Among them, f(n) represents the total cost, g(n) represents the actual cost from the starting point to the current node, and h(n) represents the estimated cost from the current node to the target; The specific implementation process of the preference setting submodule is as follows: Feature fusion, inputting the passenger's immediate needs and long-term preferences as features into a decision tree model and training the model, wherein the immediate needs are current specific conditions, including at least weather, time and location, and the long-term preferences represent passenger preference statistics based on historical data; Weight setting: set different weights for different features, and set the weight of long-term preferences higher than the weight of immediate needs; Dynamic adjustment: Dynamically adjust weight settings based on real-time feedback during decision tree model training; Model evaluation and iteration: regularly use new data evaluation models to evaluate the decision tree model, and update the parameters of the decision tree model according to the evaluation results; The model is deployed and used to prioritize passengers' different preferences using the trained decision tree model, and to make personalized pick-up and drop-off demand recommendations based on passenger historical records.

3. The autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction according to claim 2 is characterized in that: The multimodal large model optimization module includes a multimodal input processing submodule, a traffic and environment perception submodule and an intelligent optimization engine submodule; The multimodal input processing submodule is used to analyze the passenger's voice or text input through natural language processing technology to generate text input data; confirm the environmental image uploaded by the passenger through image analysis technology to generate image input data; perform data fusion processing through the conditional variational autoencoding CVAE model to generate multimodal fusion data; The traffic and environment perception submodule is used to obtain real-time traffic and environment data from traffic sensors, map APIs, and weather APIs. The real-time traffic and environment data is used to optimize the vehicle's driving path and pick-up points; The intelligent optimization engine submodule is used to predict and analyze the real-time traffic and environmental data through a multi-modal large model, and intelligently adjust the passenger pick-up and drop-off plan.

4. The autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction according to claim 3 is characterized in that: The specific implementation process of data fusion processing through the conditional variational auto-encoding CVAE model in the multimodal input processing submodule is as follows: Speech processing, using Wave2Vec to convert passengers’ speech into text data; Image processing: For the environmental images uploaded by passengers, DeepLabv3+ image semantic segmentation network is used to extract features, and the attention mechanism is integrated to enhance the recognition of objects in the environmental images to generate image semantic segmentation data; Text parsing: Use the GPT-4 model to perform semantic analysis on the text input of passengers and generate text semantic parsing data; The conditional variational autoencoding (CVAE) model embeds the text data, the image semantic segmentation data, and the text semantic parsing data into the same vector space, and realizes multimodal fusion by maximizing the similarity between related modalities. The specific implementation process of the intelligent optimization engine submodule for predicting and analyzing the real-time traffic and environmental data through a multi-modal large model and intelligently adjusting the passenger pick-up and drop-off plan is as follows: The multimodal large model includes a time coding model, a space coding model, two conditional variational autoencoding CVAE models, a fusion decoding model and a semantic BEV modal prediction model. The time coding model takes the historical behavior data of the target passenger to be predicted as input and generates time coding. The space coding model takes the multimodal fusion data as input and generates space coding. The time coding model and the space coding model will generate time-space features. The time coding model adopts a bidirectional long short-term memory LSTM network, and the space coding model adopts a DenseNet network. The two conditional variational autoencoding CVAE models include a first CVAE model and a second CVAE model; the output of the time coding model is connected to the input of the first CVAE model; the output of the space coding model is connected to the input of the second CVAE model; the fusion decoding model converts the connected time-space features into a prediction trajectory with confidence; the semantic BEV modal prediction model is used to optimize the confidence of the prediction trajectory; The real-time prediction process is: Decomposing the entire prediction task into two sub-models: a modal prediction model TPM based on modal distribution and a trajectory prediction model MPM based on modal trajectory distribution, wherein both sub-models are based on the multi-modal large model; Given historical passenger behavior data and real-time traffic and environmental data, TPM performs probabilistic inference on predicted trajectories for all modalities; The trajectory prediction model MPM of the modal trajectory distribution is used to solve the comprehensive probability of different modes and compare it with the preset probability threshold to determine whether the comprehensive probability is greater than the preset probability threshold. If so, the passenger pick-up and drop-off plan is adjusted, and the semantic BEV modal prediction model is used to optimize the confidence of each modal trajectory prediction.

5. The autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction according to claim 4 is characterized in that: The real-time path planning and dynamic adjustment module includes a real-time path planning submodule and a dynamic adjustment and optimization submodule; The real-time route planning submodule generates an optimal driving route based on the pick-up point currently selected by the passenger and the real-time traffic conditions, and recalculates the route at each change, including changes to the pick-up point and the real-time traffic conditions; The dynamic adjustment and optimization submodule is used to generate an alternative path when an emergency occurs to the vehicle, and adjust the alternative path according to the feedback from the passengers to generate a final driving path.

6. The autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction according to claim 5 is characterized in that: The specific implementation process of the real-time path planning submodule is as follows: Combine multi-sensor data and use SLAM technology for environmental mapping and self-positioning to achieve dynamic path adjustment; Based on the pick-up and drop-off points selected by passengers and real-time traffic data, the A* algorithm is used to generate the optimal path. The path planning formula uses the heuristic function: Among them, H(n) represents the estimated cost from the current node n to the target node, (x1, y1) represents the coordinates of the current node, and (x2, y2) represents the coordinates of the target node; The specific process of the dynamic adjustment and optimization submodule is as follows: When an emergency event is detected, a new path plan is generated using a genetic algorithm, wherein the genetic algorithm generates a path population through crossover, mutation, and selection operations, scores the adaptability of each path, and selects the optimal path; The alternative path generation formula is: C(x,y)=min[C(x-1,y),C(x,y-1)]+w(x,y), Among them, C(x,y) represents the minimum cost from the starting point to the current node (x,y), and w(x,y) represents the weight of the current node.

7. The autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction according to claim 6 is characterized in that: The passenger feedback and personalized recommendation module includes a personalized recommendation submodule and a real-time feedback submodule; The personalized recommendation submodule is used to preferentially recommend the pick-up and drop-off points and routes preferred by the passenger based on the passenger's historical data, wherein the historical data includes the pick-up and drop-off points used previously and the routes liked previously; The real-time feedback submodule is used to provide passengers with the vehicle's real-time location, estimated arrival time, and route suggestions through smartphones, and to adjust the pick-up and drop-off plan in real time based on the passenger's feedback; The specific implementation process of the personalized recommendation submodule is as follows: Use collaborative filtering technology to analyze passengers' historical pick-up and drop-off data, and use the matrix decomposition model to achieve personalized recommendations. The formula is: R=P*Q T , Among them, R represents the rating matrix of the pick-up point, P and Q are the feature matrices of passengers and pick-up points respectively. By optimizing P and Q, the passenger's rating of the recommended pick-up point is predicted.

8. The autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction according to claim 7, characterized in that: The intelligent accident response and safety module includes an accident response submodule and a safety priority mechanism submodule; The accident response submodule is used to automatically generate an alternative route and notify passengers to adjust the route when an accident or road closure is detected; The safety priority mechanism submodule prioritizes the safest pick-up and drop-off plan based on real-time traffic and environmental data.

9. The autonomous driving vehicle adaptive pick-up and drop-off system based on enhanced intelligent interaction according to claim 8, characterized in that: The specific implementation process of the accident response submodule is as follows: If an accident or road closure is detected, the Bayesian network is used to calculate the probabilities of different response plans and select the optimal alternative path. The formula is: Among them, P(H|E) represents the probability that hypothesis H is true when it is known that event E has occurred, P(E|H) represents the probability that event E occurs under hypothesis H, and P(H) and P(E) represent the prior probabilities of hypothesis H and time E; The specific implementation process of the safety priority mechanism submodule is as follows: The Markov decision process MDP is used to calculate the risk of each state according to the environmental data and select the solution with the highest security. The Markov decision formula is: V(s)=max a [R(s,a)+b∑ s' P(s'|s,a)V(s'))], Among them, V(s) represents the value of state s, R(s,a) represents the immediate reward of executing action a in state s, b represents the discount factor, and P(s'|s,a) represents the transition probability from state s to state s'.

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