46 results about "Traffic conflict" patented technology

The invention relates to an automatic driving risk quantification method based on a conflict risk field. Comprising the steps of 1, constructing a basic risk field; step 2, under a basic risk field framework, constructing a conflict risk field by taking an ADV as a center; step 3, regarding the conflict risk field as a repulsive force acting on the ADV, and using the repulsive force to quantify the dynamic influence of the environmental elements and various TP states on the driving safety of the ADV; the method comprises the following steps: firstly, constructing a basic risk field on the basis of traffic vehicle distribution and motion characteristics, further forming a vehicle forward, lateral, backward and traffic regulation constraint multi-dimensional conflict risk field on the basis, and converting an abstract traffic conflict relationship into a computable repulsive force model; dynamic quantitative characterization of driving risks in a state dimension, a space dimension and a time dimension is realized, and continuous optimization and evolution of an end-to-end automatic driving algorithm are supported by means of a repulsive force model constructed based on a conflict risk field.

The invention provides a mine laneway traffic scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a continuous vibration signal monitored by a distributed optical fiber sound wave sensing system in a mine laneway; performing filtering and signal identification on the continuous vibration signal to identify a vehicle driving signal; monitoring the triggering time of the calibration point in the mine laneway according to the vehicle driving signal, and determining the position and the driving direction of the vehicle; according to the vehicle driving signal, the position of the vehicle and the driving direction, determining whether vehicle early warning needs to be carried out in the mine laneway; and in response to the need of vehicle early warning, generating a vehicle scheduling and early warning instruction so as to perform vehicle scheduling. Therefore, by analyzing the vibration signals monitored by the distributed optical fiber sound wave sensing system in the mine laneway in real time, the position, the speed and the vehicle driving direction of the vehicle are accurately recognized, so that before potential traffic conflicts occur, dispatching and early warning instructions are sent to related vehicles, and the passing efficiency and the safety of the mine laneway are effectively improved.

The invention belongs to the technical field of intelligent traffic safety and risk prediction, and particularly relates to a traffic conflict risk spatio-temporal evolution prediction method based on a vehicle interaction relationship, and the method comprises the steps: taking a target vehicle as a main vehicle, and extracting the positions and speeds of the main vehicle and surrounding vehicles in a unified coordinate system through multi-source data; the method comprises the following steps: calculating a relative position and a relative speed, forming interaction relationship representation data, constructing a vehicle risk potential field on a space-time continuous domain on the basis, mapping a discrete interaction state into continuous and differentiable risk intensity distribution, further forming a sequence by the risk potential fields at each moment, introducing a space-time coding module based on Transform, and coding the vehicle risk potential fields at each moment by using a time-space coding module based on the Transform. And combining position coding, self-attention and graph attention to represent time sequence dependence and multi-vehicle risk propagation coupling, and outputting future risk field evolution distribution. According to the method, the problems of information loss and insufficient spatio-temporal expression of a traditional single time interval index and fixed time window method are solved, conflict risk dynamic prediction is realized, and a reliable basis is provided for traffic safety management and active intervention.

The invention discloses a motor vehicle chain conflict identification method based on a rear vehicle avoidance characteristic, and the method comprises the steps: 1, collecting the driving track data of motor vehicles on an urban road, calculating the collision time, and identifying a single traffic conflict between the motor vehicles on the same longitudinal lane and between the motor vehicles on adjacent transverse lanes; 2, on the basis of a stimulation-reaction model, determining a vehicle avoidance reaction threshold by combining a signal detection theory, and identifying an avoidance behavior of a vehicle after the conflict; and 3, merging conflicts with continuous avoidance behaviors, dynamically delimiting the influence duration of the conflicts, and constructing a complete chain conflict chain based on causal association between the conflicts. According to the method, the defect that space-time proximity is equivalent to causal relevance in the prior art is overcome, the accuracy of chain conflict recognition is improved, and reliable support is provided for road traffic safety early warning and management and control.

The invention discloses an intersection state sensing and traffic safety evaluation method based on an unmanned aerial vehicle and a large model, and belongs to the field of traffic safety evaluation. Comprising the following steps: constructing a target detection and tracking model based on an unmanned aerial vehicle visual angle, collecting intersection state and traffic flow data by using an unmanned aerial vehicle aerial video, counting traffic conflict data, constructing intersection potential conflict indexes, constructing a comprehensive grade evaluation model, improving an initial model by using a large model, and outputting an intersection comprehensive safety evaluation grade. Using an improved RT-DETR algorithm and a Deepsort model to improve the detection precision of the traffic participants and carrying out training; the intersection state and traffic flow data are extracted, and the overall risk quantification of the intersection is realized through a plurality of potential conflict indexes and by adjusting the weight of each index through a large model. According to the method, dynamic regulation and control are carried out on the safety index weight in combination with a large model, comprehensive evaluation of potential risks, abnormal behaviors and the overall safety level of the intersection is achieved, and the method has important significance.

This invention relates to an AEB (Autonomous Emergency Braking) triggering method, device, and medium based on traffic conflict, comprising the following steps: identifying at least one target vehicle in the environment; determining the vehicle type of the target vehicle based on a preset target detection model; matching a preset target vehicle mass based on the vehicle type; calculating the difference in braking distance based on the acquired relative distance between the vehicle and the target vehicle and the target vehicle speed; calculating the collision loss momentum based on the target vehicle speed, the target vehicle mass, the acquired vehicle speed, and preset vehicle mass information; calculating the risk level based on the difference in braking distance and the collision loss momentum, and according to the vehicle type; and triggering AEB when the risk level meets predetermined conditions. Compared with the prior art, this invention ensures vehicle driving safety while also considering comfort.

The invention discloses an intelligent road cone and a vehicle dynamic guiding method, and belongs to the technical field of intelligent traffic facilities, the intelligent road cone comprises a cone structure, a counterweight base, a sensing unit module, a microcontroller, a communication module and a warning module.The inclination state is monitored through a posture detection assembly, a sensing unit collects multi-dimensional data, and the warning module sends the multi-dimensional data to the microcontroller; according to the guiding method based on the road cone, coherent electromagnetic waves are emitted through the intelligent road cones on the two sides of a road to form an interference enhancement area, a repulsive force-gravitational force virtual force field model is constructed in combination with multi-channel signal detection, and the interference enhancement area and the repulsive force-gravitational force field model are combined. And guiding the vehicle to drive towards a safe area by using the control logic that the gravitation weight is dynamically higher than the repulsive force. According to the invention, hot plug expansion is realized by adopting a modular architecture, the structural stability and the function expansibility are enhanced, the vehicle guiding precision is improved through dual-source electromagnetic wave interference and an intelligent control algorithm, the traffic conflict risk in a construction area is effectively reduced, and the road construction safety and the management efficiency are improved.

The application discloses a kind of dangerous event chain extraction method and system based on complex traffic scene, including obtaining test scene data;Based on the key factors of dynamic attribute and interaction between vehicle and environment determined by preset natural driving data and sensor performance information, and the uncertainty of key factors is quantified;Based on the risk estimation multidimensional feature set and uncertainty information preset constructs driving risk estimation model for test scene;The driving risk estimation model is optimized, and the optimized risk estimation model for test is obtained;Based on the time evolution characteristics of dangerous event, a dangerous time chain model is constructed according to the optimized risk estimation model;Dangerous time chain model is solved, and dangerous event chain is obtained.The application uses traffic accident data and traffic conflict data to reveal the space-time evolution law of complex traffic scene, and improves the effect of later test scene reproduction and reconstruction by solving the dangerous time chain model.

This invention provides a short-term traffic conflict prediction method and early warning system based on RSU group collaboration and spatiotemporal graph neural network. It acquires vehicle trajectory and status data collected collaboratively by roadside unit groups, using the coverage area of ​​each RSU as a spatial analysis unit, and constructs an initial energy field distribution map of traffic conflict based on the spatial density of vehicle distribution and the intensity of speed temporal fluctuations. A pre-constructed spatiotemporal graph neural network is invoked to capture energy transfer paths between units, and a temporal cyclic layer is used to capture the energy change inertia within units, predicting and generating a dynamic evolution map of traffic conflict energy containing an energy evolution path network and peak times. Based on the map, potential conflict outbreak sources and expected affected unit sequences are identified, generating conflict propagation situation prediction information including spatial coordinates and propagation time sequences. Based on this information, differentiated collaborative working mode adjustment instructions are generated and distributed to each RSU. This invention achieves short-term prediction of traffic conflicts and collaborative scheduling of roadside resources.

The invention provides an open block traffic flow intelligent distribution method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the processing of a basic triangular unit, so as to define the interaction range of multi-mode traffic flow; according to the interaction range, mapping the point cloud data on the virtual analysis plane to a corresponding actual road network area so as to extract mutual interference parameters of multimode traffic flows in the branch network, and generating a Gaussian mixture model initial parameter set; and based on the initial parameter set of the Gaussian mixture model, carrying out iterative optimization distribution on the open block OD pair, dynamically adjusting the path selection probability to obtain a final flow distribution scheme, and outputting the real-time flow distribution proportion of each path. According to the method, key features can be extracted from mass trajectory data, traffic conflict areas are accurately positioned in space, and interference intensity is quantified.

This invention relates to the technical fields of tunnel safety control, intelligent transportation, vehicle-road cooperation, and tunnel safety early warning. Specifically, it relates to a tunnel active safety control system and method based on vehicle-road cooperation. Key technical points include: a multi-source data fusion module: integrating data from roadside equipment (including radar, cameras, and fiber optic sensors) and vehicle-mounted terminals to construct a four-dimensional perception network of people, vehicles, roads, and the environment; a dynamic risk assessment module: quantifying the probability of an accident (P) and collision energy loss (E) based on an equivalent traffic conflict number (ECN) model to achieve risk-level early warning; and a vehicle-road cooperative intervention module: triggering early warning and control strategies from both the roadside and vehicle perspectives through two-way communication between roadside smart guidance signs, information boards, and vehicle-mounted terminals. This invention improves the ability to respond to tunnel risks.

The invention discloses a perception analysis system based on intelligent management. An operation method of the system comprises the following steps: performing information input and data processing of an intelligent network connection automobile; detecting, tracking, analyzing and processing the intelligent networked automobile; carrying out intelligent network connection automobile traffic conflict analysis, discrimination and management; and carrying out statistics on the user data information of the intelligent networked automobile. The information input and data processing of the intelligent connected automobile comprises the following steps: inputting destination information to the system through the sensing analysis system before the user of the intelligent connected automobile drives each time; distortion elimination processing is carried out on the image information collected by the intelligent network connection automobile equipment; after the laser video all-in-one machine in the intelligent networked automobile completes acquisition of driving data, coordinate system conversion processing is carried out on the driving data. The step of detecting, tracking, analyzing and processing the intelligent networked automobile comprises the step of detecting the position information of the intelligent networked automobile in real time. The system has the characteristics of intelligent analysis and management and high safety.

The invention provides a signal and vehicle cooperative intelligent control method and system based on traffic conflicts, and the method employs a double-layer cooperative optimization control architecture: an upper-layer traffic signal control model dynamically adjusts the duration of a green light; according to the lower-layer vehicle track control model, the length of a conflict area is calculated in combination with the maximum braking distance of the vehicle according to traffic conflicts caused by bus entering and exiting, conflict recognition conditions and conflict elimination constraints are provided, and on the basis, the acceleration of a vehicle team pilot vehicle is decided with the fuel consumption and comfort of the vehicle as optimization targets. According to the method, a double-layer optimization control model is solved by adopting dynamic planning, green light duration and vehicle tracks are adjusted in real time in combination with vehicle-road cooperative communication, bus priority passing and safe and orderly avoidance of common vehicles can be guaranteed, average passenger delay and travel time of buses in a conflict area are effectively reduced, and the traffic safety of the buses is improved. The overall oil consumption is reduced; and the intersection passing efficiency is improved.

The application provides an airport flight area crossing traffic command system based on an A-SMGCS system, and belongs to the technical field of intelligent traffic, and comprises: a sensor layer, which is used for collecting aircraft dynamic data, to-be-passed authorization object data, environmental meteorological data and dynamic obstacle data; a data processing and decision-making layer, which is used for standardizing processing and space-time mapping processing of the collected data, judging existing potential conflicts in combination with a dynamic threshold adjustment mechanism, and generating and issuing instructions based on multi-objective optimization decision-making; and an execution layer, which is used for controlling crossing traffic control equipment according to the issued instructions, and feeding back execution results to the data processing and decision-making layer in real time to form a closed-loop control. The application effectively reduces the traffic conflict risk of the crossing of the airport flight area, improves the crossing efficiency, reduces the vehicle waiting time and the aircraft taxi delay, adapts to the fine scheduling demand of the airport scene movement, and provides reliable support for the safe and efficient operation of the airport flight area traffic.

The invention provides a heterogeneous traffic flow-oriented expressway deceleration lane length optimization calculation method. A deceleration lane is divided into a deceleration section, a transition section and an auxiliary lane section; designing a simulation experiment, generating a plurality of groups of heterogeneous traffic flow working condition combination sets based on orthogonal experiment design, and outputting traffic operation data of each working condition under a microscopic simulation platform; wherein the traffic volume, the shunting vehicle proportion and the automatic driving vehicle permeability are represented; constructing a reward function by taking the serious traffic conflict rate, the relative traffic efficiency and the speed variation coefficient as evaluation indexes; and calculating a reward function through the heterogeneous traffic flow working condition combination set, and carrying out optimization solution on the deceleration section, the transition section and the auxiliary lane section by adopting a search optimization algorithm to obtain optimal lane lengths of the deceleration section, the transition section and the auxiliary lane section.

A projection control method, a data processing method, a projection system and related products are applied to the technical field of projection. The projection control method comprises the steps that a control device can obtain a first position indication or determine the first position indication based on environment information around a terminal, a projection module of the terminal is enabled to project a prompt icon through the first position indication, and the projection position of the prompt icon is fixedly located at the position indicated by the first position indication; the prompt icon is used for indicating the terminal to approach the traffic conflict area. By resolving the fixed projection position and projecting the icon at the fixed projection position, the continuous prompting capability of the terminal in the traffic conflict scene can be improved, the warning effect of the projected image is enhanced, and the safety of the terminal is improved.

The application provides an open block traffic flow intelligent distribution method and system, and relates to the technical field of data processing.The method comprises the following steps: processing a basic triangular unit to define the interaction range of multi-mode traffic flow; according to the interaction range, mapping point cloud data on a virtual analysis plane to a corresponding actual road network area to extract multi-mode traffic flow interference parameters in a branch network, and generating a Gaussian mixture model initial parameter set; based on the Gaussian mixture model initial parameter set, iteratively optimizing and distributing open block OD pairs, dynamically adjusting path selection probability, obtaining a final traffic distribution scheme, and outputting real-time traffic distribution proportions of each path.The application can extract key features from massive trajectory data, accurately locate traffic conflict areas in space, and quantify interference intensity.

The application discloses a kind of intersection safety state perception and diagnosis management system and method based on unmanned aerial vehicle video, system includes video input module, index extraction module, safety evaluation module, conflict discrimination module, hidden danger diagnosis module, measures management module and result output module.The application proposes an improved deceleration DRAC calculation method for avoiding conflict, which is suitable for various types of traffic conflict events in the database;The system can automatically detect the intersection safety level, and automatically generate management measures for problem intersections, realize the precise strategy and fine management of intersection, effectively reduce the intersection accident, and improve the intersection safety level.

The present application relates to a kind of riding risk perception test methods based on immersive virtual reality interactive platform, the method is by constructing virtual reality traffic scene, simulation builds immersive virtual reality interactive test platform, let rider simulate real riding behavior in virtual scene, by recording the speed, acceleration and other riding data of rider, jointly use the objective data of riding behavior in virtual reality and risk perception subjective data to carry out risk assessment, construct dynamic risk assessment model applicable to immersive virtual reality interactive platform, and study the reaction behavior of rider under traffic conflict scene, test the perception and response ability of rider to riding risk.The present application can provide theoretical basis for slow traffic safety analysis, and the test results can be targeted to propose safety suggestions, so as to improve the traffic safety consciousness of rider and reduce the occurrence of traffic accident.

The invention provides a control method and a control system for intelligent car traffic. Determining an expected time window of the target task at the corresponding intersection through a target planned path of the target task, a task completion time window and a preset average driving speed value; determining a reservation time window of the target task at the corresponding intersection at least based on the expected time window of the target task at each intersection and a window occupation table of the corresponding intersection; determining a target intelligent trolley executing the target task; and controlling the target intelligent car to pass through the corresponding intersection in the reservation time window of each intersection according to the target task. Through the reservation time window of each task in the window occupation table, the passing sequence of the intelligent cars executing the tasks is effectively controlled, traffic conflicts of the intelligent cars at the intersection are avoided, and the passing rate of the intersection is improved.

The application relates to the technical field of intelligent warehousing, and discloses a method for AGV and automated stereoscopic warehouse collaborative scheduling, which comprises the following steps: S1, collecting stereoscopic warehouse entrance and exit coordinates, channel layout parameters, AGV real-time positions, speeds, electric quantities and warehouse task data, and constructing a basic data pool; S2, based on historical task time length, stacker states and road conditions, adopting a time sequence prediction model to predict cargo arrival time, and demarcating a docking time window; S3, detecting AGV position deviation through visual recognition, and dynamically adjusting the vehicle body and the taking and placing device posture; S4, summarizing the states of multiple AGVs, the docking window and the task priority, adopting a global path planning and dynamic traffic right distribution algorithm to plan a collaborative path; S5, driving along the planned path, matching the docking time window and adjusting the vehicle speed, and avoiding traffic conflicts; and S6, counting the docking success rate and the collaborative traffic efficiency. The method improves the warehousing cargo flow efficiency and quality through data pool construction, accurate prediction, dynamic adjustment and collaborative planning.

This invention discloses an intelligent system for generating green wave maps at intersections using traffic flow pattern calculations, relating to the field of traffic management. The device is deployed at intersections and integrates a directional display screen, a composite optical and radar camera, traffic lights, and a control unit, along with various types of waiting areas, auxiliary traffic lights, and ground-based LED guide lights. The system collects real-time information on vehicle flow speed, length, and position, dynamically matching green light durations and rationally allocating passage time. This invention uses urban center intersections as the origin of green wave growth, repairing green wave gaps through traffic flow pattern alignment calculations and adaptive vehicle speed adjustment, gradually extending to form a full-area, two-way dynamic green wave map. This invention solves the problems of limited coverage, complex calculations, poor self-healing capabilities, and weak coordination of traditional green wave systems, optimizing intersection traffic flow organization, avoiding traffic conflicts, achieving full-area collaborative control of traffic signals, and effectively improving the efficiency and safety of urban road networks.

The application relates to a traffic conflict risk assessment method combining multi-dimensional conflict indicators and a cloud model, which comprises the following steps: obtaining original trajectory data of a vehicle and cleaning the trajectory data; then, effectively identifying traffic conflicts based on a safety distance threshold and a time threshold; continuing to construct three-dimensional traffic conflict measurement indicators according to the effective traffic conflict trajectory data; determining the cloud model digital features of each conflict measurement indicator, establishing a traffic conflict severity assessment cloud model, calculating the mean value of the membership degree, and obtaining the weight of each conflict evaluation indicator in the comprehensive risk assessment by using the principal component analysis method; finally, based on the mean value of the membership degree and the weight value, the conflict is classified according to the weighted membership degree and the confidence degree criterion, and the traffic conflict severity classification is determined. The application can more comprehensively depict the real dynamic changes of the vehicle and the conflict risk severity, provides an efficient data processing flow and a modeling framework for the non-motor vehicle red light running conflict risk assessment, and provides scientific theoretical support and technical reference for the urban intersection traffic safety optimization and the control strategy formulation.

The invention provides a navigation method for an intelligent trolley to enter a buffer position, a production line and a control system. According to the invention, the buffer position of the vehicle door buffer area is subjected to partition and level-to-level management, and the predicted buffer duration of the current intelligent vehicle waiting at the entrance of the buffer area is obtained through the production task; and determining a target callout level of the current intelligent car and a target buffer subarea corresponding to the target callout level according to the predicted buffer duration of the current intelligent car, and finally, pausing the current intelligent car to a target buffer position in the target buffer subarea according to a call-in navigation instruction. The pause sequence of the intelligent trolley in the target buffer subarea is optimized, traffic conflicts in the driving-out process of the intelligent trolley are reduced, and the driving-out efficiency and the conveying efficiency of the intelligent trolley with the high driving-out level are improved.

The invention provides a real-time traffic conflict prediction method based on retrieval enhanced deep learning. On the basis of a deep learning multi-layer perceptron (MLP) model, a lightweight retrieval enhancement module is embedded, and a retrieval enhancement multi-layer perceptron (MLPR) model is constructed; according to the module, historical information similar to input data is retrieved and fused in an external knowledge base, so that the prediction precision and generalization ability of the model are improved. The result shows that compared with the MLP model, the accuracy and the sensitivity of the MLPR model are respectively improved by 6.6% and 12.2%; under the condition of small samples, the prediction performance of the MLPR model is greatly improved. The retrieval enhanced deep learning-based real-time traffic conflict prediction method provided by the invention is suitable for a vehicle-road cooperation (V2I) or vehicle-road cloud integration (VRC) environment, and can provide technical support for active safety real-time early warning and management and control.

The present application relates to a non-stationary extreme value and Bayesian deep learning accident risk prediction method and system, belonging to the technical field of traffic engineering. The method comprises data acquisition, data processing after model construction, mapping transformation of original parameters and calculation of collision risk as a quantitative safety index. The system comprises an input module, a deep learning module, a GEV parameter mapping module and an accident risk prediction module. The present application combines deep learning with non-stationary extreme value modeling and proposes a Bayesian deep learning driven non-stationary generalized extreme value distribution (GEV) parameterization method, realizing real-time accident risk prediction based on traffic conflicts. The method can learn the complex nonlinear relationship between the covariates and the distribution parameters, provide a data-driven parameterization method, accommodate more covariates and consider the time dependence of the covariates.

The invention relates to the technical field of traffic avoidance, in particular to a traffic conflict point active collision avoidance control method and system, and the method comprises the steps: carrying out the dynamic conflict point recognition of a road intersection, and obtaining a real-time traffic conflict point distribution diagram; performing behavior feature extraction on the traffic participants according to the real-time traffic conflict point distribution map to obtain dynamic state data of the traffic participants; inputting the dynamic state data of the traffic participants into a traffic conflict risk assessment network for risk quantification processing to obtain a traffic conflict risk assessment result; and performing conflict development trend prediction processing on the traffic conflict risk assessment result to obtain a traffic conflict evolution prediction result. Through cooperative control with infrastructures such as a traffic signal control system and a traffic guidance system, the traffic light time length, lane distribution and the like can be intelligently optimized, the efficiency of a traffic management system is improved, and the problems of traffic bottleneck and congestion are avoided.

This invention discloses a vehicle trajectory stitching method based on cross-correlation time synchronization and two-stage matching, comprising: 1. cleaning and fragment reconstruction of multi-source structured trajectory data; 2. achieving high-precision time synchronization by setting virtual detection lines in overlapping areas and analyzing the cross-correlation of traffic signals; 3. matching trajectories with time overlap using a dynamic time warping algorithm, and matching non-overlapping trajectories using a progressive relaxation method; 4. dynamically weighting and fusing the matched trajectories based on a smoothing weight function to ensure a smooth transition at the stitching point; and 5. applying Kalman filtering for overall trajectory smoothing and denoising. This invention achieves efficient and accurate trajectory stitching without relying on the original video, solving the technical problems of difficult spatiotemporal alignment of multi-source asynchronous trajectory data and easy jumps at the stitching point, providing high-quality long-distance trajectory data support for microscopic traffic behavior analysis, traffic conflict identification, and traffic safety assessment.

The invention provides a driving-out sequence adjusting method, a buffer area and a control system. According to the method, the target insertion path is determined according to the position information of at least one low-position intelligent car, the position information of the target intelligent car and the real-time buffer map, and the insertion navigation instruction of the target intelligent car is generated based on the target insertion path. The target insertion path is obtained through flexible adjustment by utilizing the two driving channels and the free buffer positions in front, and finally the target intelligent car is adjusted to the better driving-out positions on the driving channels, so that traffic conflicts are avoided.

A rule violation leading to a traffic conflict involving a vehicle and at least one agent in real-time driving scenarios, simulation, re-simulation or other application is determined to be the fault of the vehicle by determining whether the situation was “reasonably foreseeable,” determining whether the at least one agent is a vulnerable road user (VRU) and determining whether the at least one agent violated a higher priority rule than the rule violated by the vehicle. In an embodiment, on-vehicle decisions are based on a rulebook with a priority structure that accounts for responsibility and determines an “initiator” of the traffic conflict based on whether the vehicle or the at least one agent was the first to violate a rule and the first to violate a higher priority rule.