655 results about "Vehicle behavior" patented technology

The invention provides an intelligent traffic vehicle behavior modeling method and system based on collaborative learning, and relates to the technical field of unmanned driving, and the method comprises the steps: obtaining traffic data in a target region; performing data calibration processing according to the traffic data to obtain a collaborative perception data set; a vehicle space-time trajectory function is generated according to the collaborative perception data set, and a conflict domain space-time distribution diagram is obtained; according to the vehicle space-time trajectory function and the conflict domain space-time distribution diagram, probability distribution characteristics of the vehicle driving intention and a multi-workshop competition cooperation relation matrix are extracted, and a multi-dimensional interaction characteristic vector is obtained; and behavior modeling is carried out according to the multi-dimensional interaction feature vectors, and a vehicle behavior model representing interaction behavior rules and decision logic between vehicles is obtained. According to the method, through multi-vehicle cooperative sensing data calibration and dynamic fusion of road geometric constraints, the problems of single-vehicle sensor shielding and cross-vehicle data heterogeneity are effectively solved, and the global consistency of vehicle position and motion state estimation is remarkably improved.

The invention discloses an AR-fused remote driving vehicle virtual-real interaction hardware-in-the-loop test system, particularly relates to the technical field of automatic driving test, and is used for solving the problems of inaccurate coupling between a virtual scene and a real vehicle behavior and lack of AR prompt response evaluation. The method comprises the following steps: firstly, constructing a dynamic obstacle intention-driven prediction model based on time series data of a multi-modal sensor, and generating a trajectory probability distribution and risk thermodynamic diagram; then, space-time alignment of the virtual accident scene and the real environment is achieved through a dynamic binding algorithm, and the virtual-real shielding priority of an AR interface is dynamically adjusted; by simulating abnormal disturbance of a vehicle actuator, synchronously collecting control and watching responses of a driver, and extracting obstacle avoidance path deviation degree and takeover timeliness parameters; and finally, separating and compensating virtual and actual residual errors based on a path deviation index, realizing online correction of a virtual scene attitude and a dynamic trajectory, constructing a closed-loop optimization mechanism, and improving the precision and stability of a test system.

The invention relates to the technical field of traffic communication control, and discloses a vehicle-road cooperative communication optimization system for intelligent traffic. The system comprises a multi-source data sensing module for collecting multi-source heterogeneous data; the communication feature extraction module is used for analyzing the multi-dimensional features; the space-time fusion modeling module is used for constructing a combined space-time feature space; the double-layer resource scheduling library is used for storing an optimization strategy; and the dynamic collaborative decision module is used for generating a real-time scheme and instruction. The method also relates to the functions of abnormal track detection, path re-planning, communication link stability prediction and the like. According to the system, deep fusion and efficient processing of multi-source data are realized, communication resource scheduling and traffic flow regulation and control are optimized, abnormal behaviors of vehicles can be processed in time, the stability of a communication link is guaranteed, the operation efficiency, safety and communication reliability of an intelligent traffic system are effectively improved, and intelligent traffic development is promoted.

The invention relates to the technical field of smart parking lots, in particular to a vehicle safety management method based on a smart parking lot, which comprises the following steps: S1, multi-source data acquisition: acquiring dynamic trajectory data of a target vehicle, parking lot environment parameters and adjacent parking space state data in real time, and generating a multi-source feature vector set; s2, data space-time alignment: carrying out space-time alignment processing on the multi-source feature vector set, and outputting a standardized vehicle behavior sequence; s3, abnormal behavior recognition: inputting the standardized vehicle behavior sequence into an abnormal behavior recognition model, and generating an abnormal behavior characteristic spectrum and a confidence coefficient parameter; s4, risk level assessment: constructing a risk assessment model based on the confidence coefficient parameter and the environmental parameter, and outputting the risk level of the parking lot in the current scene; and S5, security policy triggering: triggering a corresponding security policy execution instruction according to the risk level. According to the invention, potential risks can be effectively prevented, and the safety of vehicles and personnel is ensured.

The invention relates to the technical field of unmanned aerial vehicle detection, in particular to an anti-unmanned aerial vehicle detection method and system, and the method comprises the following steps: scanning a target airspace in real time through broadband cognitive radio, and constructing an environment feature matrix; based on the environment feature matrix, double-flow feature extraction is carried out, and a protocol fingerprint vector is generated; activating a corresponding group behavior analysis model according to the protocol fingerprint vector, and constructing a behavior topological graph; constructing a dynamic threat vector by adopting a game theory in combination with the behavior topological graph and the real-time motion parameters; generating a multi-dimensional interference strategy set according to the dynamic threat vector; according to the invention, an omnibearing and real-time updated threat vector is constructed, a node threat value is calculated through a non-cooperative game model, accurate evaluation is carried out in combination with spatio-temporal information, potential threats are effectively identified, accurate interference on an unmanned aerial vehicle group is realized, the perception capability of unmanned aerial vehicle behaviors in a complex scene is enhanced, and the risk of the unmanned aerial vehicle behavior in the complex scene is reduced. And the execution precision and efficiency of the interference strategy are obviously improved.

The invention relates to the technical field of intelligent driving, and discloses a user service recommendation method for a vehicle-mounted scene, and the method comprises the steps: collecting the vehicle-using behavior data of a user, carrying out the correlation marking of the intention of the user through a multi-dimensional marking system, carrying out the vectorization, and storing the vectorized intention into a vector database; capturing real-time behavior data of a user to generate a real-time behavior vector; based on the vector database, generating an intention context corresponding to a real-time behavior vector through a hybrid retrieval strategy; and generating a prompt project based on the intention context, inputting a large model output service recommendation result, and pushing the result to the user. By integrating mechanisms such as data annotation, vector retrieval and large model reasoning, the problem of long cold start period depending on long-term buried point data learning in the prior art is solved, potential intentions are quickly matched, the system effective time is shortened, and the service recommendation accuracy based on the user intentions is effectively improved. The invention further discloses a user service recommendation device for the vehicle-mounted scene, the vehicle and a computer readable storage medium.

The invention relates to the technical field of highway traffic safety guarantee, and particularly discloses a highway early warning system and an early warning equipment layout method under ice and snow conditions. The problems that recognition lags, the early warning response has the regional splitting phenomenon and the early warning information has deviation in the common technology are solved. Comprising a data acquisition layer which is used for acquiring meteorological data, road surface data, traffic flow data and vehicle behavior data in real time and eliminating abnormal data through a cross validation mechanism; the analysis decision-making layer analyzes the fused multi-dimensional data based on a multi-factor coupling dynamic risk assessment model, and generates a blue / yellow / orange / red four-level early warning signal; and the early warning issuing layer is used for issuing early warning information according to the early warning grade matching equipment combination. Through multi-source data cross validation and feature fusion, the false report and missing report rate is remarkably reduced, and the risk identification accuracy is improved.

The disclosure relate to detecting differences indicative of non-determinism in software used to control autonomous vehicles. For instance, a simulation may be run using the software at least two times. The output of the simulations may be tracked. The output of the simulations includes messages may be generated by a decision making module of the software that affect vehicle behavior. A pair of messages generated from each of the at least two times the simulation is run may be compared. Based on the comparison, a difference indicative of non-determinism in the software may be detected.

The invention relates to the field of intelligent automatic driving, in particular to a multi-vehicle cooperative controllable confrontation test method based on a diffusion model. Comprising the following steps: S1, a diffusion model training stage: training a diffusion model based on real driving data, and learning vehicle behavior distribution through forward noise addition and reverse denoising to obtain fixed model parameters; s2, scene and multi-vehicle space-time modeling: fusing the road map, the lane topology and the vehicle state information, and constructing a space-time scene representation of multi-vehicle interaction as input of diffusion generation; s3, a diffusion generation mechanism based on adversarial guidance; s4, performing partial diffusion control and diversified confrontation generation; and S5, evaluating the authenticity and controllability of the multi-agent confrontation scene. Compared with the prior art, the method has the advantage that the authenticity, controllability and closed-loop consistency of multi-vehicle cooperative confrontation scene generation are obviously improved.

The invention discloses a tunnel entrance dynamic light and signal control system based on a visual algorithm, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of poor light control when the visual field of a tunnel entrance suddenly changes. According to the method, a dynamic sensing mechanism based on visual feature understanding and multi-strategy linkage is constructed, continuous monitoring and intelligent control over the driving visual adaptation state of the tunnel portal are achieved, multi-dimensional visual features are extracted through image collection and preprocessing, visual risk scores are generated, visual field sudden change and traffic abnormity are recognized in combination with a sliding window, and the driving visual adaptation state of the tunnel portal is obtained. A risk response instruction is output, illumination adjustment and signal linkage control are driven, then vehicle behavior continuity, parking probability and blind area shielding factors are fused, speed limiting, guiding or warning signals are dynamically issued, and visual model fine adjustment and strategy updating are performed based on execution feedback. And finally, illumination and signal adaptive optimization under cross-time and multi-environment conditions is realized in combination with a control scene library, and the traffic safety and control intelligence level are improved.

The invention discloses a road transportation government affair safety supervision and management method, and relates to the technical field of road transportation safety supervision data processing. The vehicle and road safety supervision and management method comprises the following steps: collecting passing driving dynamic information from any traffic observation device; the vehicle behavior monitoring device and the road surface state monitoring device are integrated, the vehicle driving behavior data and the road safety condition data are collected in real time, and the monitoring frequency is dynamically adjusted according to the space interval, the abnormal information and the historical data, so that the utilization efficiency of monitoring resources is improved, and the monitoring cost is reduced. The system collects historical data and real-time abnormal data of vehicle driving behaviors and road safety conditions, then a primary space and a secondary space are calculated through a calculation and analysis engine, corresponding correction coefficients are determined according to intervals and historical safety condition information, then the observation frequency of the monitoring device is adjusted, and the monitoring accuracy is improved. And resource allocation is optimized while the detection coverage rate of the system is ensured.

The invention discloses a vehicle trajectory prediction method based on physical social soft attention Transformer, and the method comprises the steps: constructing a scene attention sharing mechanism for the condition of physical environment information loss in a conventional method, and enhancing the influence of a physical environment on attention; aiming at the condition of lacking complex vehicle behavior information, constructing a social soft attention module to learn a vehicle track intention from the vehicle interaction information; constructing a Transform network of a local-global attention mechanism to capture global and local scale features in allusion to the situation that local features of the vehicle are missing; inputting the trajectory coordinates of the vehicle and the physical scene features into a Transform network to obtain high-level spatial-temporal features under multiple factors; and then, a physical social soft attention Transform network is constructed. According to the method, through learning of complex vehicle behavior information and a physical scene, the prediction precision of a vehicle track prediction task in the complex scene is enhanced, the ability of the model to extract multi-dimensional spatial-temporal characteristics of the track is enhanced, and the prediction performance of track prediction is improved.

The invention belongs to the technical field of automatic driving, and relates to a virtual simulation dynamic path test method based on a fixed trajectory of an accident scene, which comprises the following steps: S1, collecting and preprocessing multi-source data, analyzing the data to generate point cloud information and visual information, and extracting vehicle parameter information, vehicle behavior information, environment information and traffic participant abnormal behavior information; the vehicle behavior information comprises a fixed track; s2, a virtual simulation scene is constructed, a virtual three-dimensional scene model is constructed according to the point cloud data and the visual information, and parameters of the virtual three-dimensional scene model are set according to the vehicle parameter information, the vehicle behavior information, the environment information, the traffic participant abnormal behavior information and the traffic rules; s3, testing by taking the fixed track as a reference track, and performing dynamic path adjustment by utilizing a path planning algorithm in an automatic driving system during testing; and S4, analyzing the test data, evaluating from multiple dimensions, generating an analysis report, and optimizing suggestions. The problem of inaccurate test results is solved.

The invention discloses an unmanned aerial vehicle behavior recognition method based on multi-modal fusion and a space-time enhancement network, and relates to the technical field of computer vision and artificial intelligence, and the method comprises the steps: extracting human body joint point coordinates frame by frame from an input unmanned aerial vehicle video through employing a lightweight attitude estimation model, and generating an initial skeleton sequence; performing enhancement processing on the initial skeleton sequence, and inputting the enhanced skeleton sequence into a double-branch space-time enhancement network to perform space-time feature extraction; the method comprises the following steps: detecting and segmenting objects in a scene from an unmanned aerial vehicle video by utilizing a grouping visual model, and obtaining a pixel-level object mask and a semantic category thereof; constructing a space-time dynamic graph network based on the detected object; performing cross-modal fusion on the spatio-temporal features and the spatio-temporal dynamic graph; and classifying the fused features, and outputting a final recognition result. According to the method, the behavior recognition robustness in the low-quality video is remarkably improved, interference caused by shielding and multi-view change is effectively relieved, and the ability of understanding human-object interaction semantics is enhanced.

The invention relates to the technical field of parking resource scheduling, in particular to an intelligent parking resource scheduling system based on multi-source data analysis, which realizes full-scale digital mapping from vehicle individual behaviors and parking lot flow fields to a regional traffic network by constructing microscopic-mesoscopic-macroscopic three-level parking twin bodies and fusing dynamic and static multi-source data. Through scale conversion and data interaction among the three-level model, dynamic characteristics of parking resources in space and time dimensions are accurately captured, a high-fidelity and fine-grained digital twinborn base is provided for scheduling decision making, splitting of local optimization and global collaboration in traditional scheduling is broken through, the accuracy and foresight of resource allocation are supported, and the scheduling efficiency is improved. Based on dynamic construction of an algorithm strategy library and twinborn simulation evaluation, the scheme takes a multi-dimensional scheduling value as a core to realize closed-loop optimization of algorithm combination-effect rehearsal-strategy optimization-dynamic adjustment.

A method for behavior prediction of vehicles in a scene can include: recording a set of observations, determining a scene graph, determining a set of scene features, predicting agent behavior based on the scene graph, and / or controlling an autonomous vehicle. The method functions to determine vehicle controls for an autonomous vehicle based on elements in the surrounding environment and relationships between the elements.

The invention provides a trajectory prediction method and device based on behavior intention of an unmanned aerial vehicle, a device and a medium, and the method comprises the steps: employing a random forest model to screen multi-source state information of the unmanned aerial vehicle, obtaining flight state key features, and completing feature importance evaluation and precise screening; building a multi-classification maneuvering intention recognition model by adopting a support vector machine based on the flight state key features, and obtaining probability distribution of various maneuvering actions of the unmanned aerial vehicle based on the multi-classification maneuvering intention recognition model; a neural network trajectory prediction model of a coding-decoding structure with a bidirectional gating circulation unit is constructed, probability distribution of various maneuvering actions of the unmanned aerial vehicle is introduced, the probability distribution is used as key semantic constraints to be embedded into a trajectory prediction algorithm, a technical link of multi-link collaborative optimization is formed, and a prediction result of a future trajectory of the unmanned aerial vehicle is obtained. And high-precision prediction of the track of the unmanned aerial vehicle in a complex environment is effectively realized.

The invention belongs to the field of automatic driving technology and man-machine interaction, and discloses a multi-modal data-based intelligent driving vehicle riding comfort evaluation method, which comprises the following steps of: acquiring vehicle dynamic performance data, vehicle driving behavior information and passenger human body physiological indexes through a sensor, and collecting subjective scores of passengers at the same time; and preprocessing the collected data, eliminating invalid physiological reactions, carrying out subjective score consistency verification, carrying out association analysis and screening valid data, and carrying out image conversion to form a final training sample. A systematized automatic driving vehicle comfort evaluation model is constructed, and vehicle dynamic performance, physiological response and subjective feeling are comprehensively considered. According to the systematized evaluation model for the riding comfort of the autonomous vehicle, vehicle behaviors and objective responses and subjective feelings of passengers are integrated into an evaluation system, and the systematized evaluation model is more accurate and reliable.

Recent location and control information received from “lead” vehicles that traveled over a segment of land, sea, or air is captured to inform, via aggregated data, subsequent “trailing” vehicles that travel over that same segment of land, sea, or air. The aggregated data may provide the trailing vehicles with annotated road information that identifies obstacles. In some embodiments, at least some sensor control data may be provided to the subsequent vehicles to assist those vehicles in identifying the obstacles and / or performing other tasks. Besides, obstacles, the location and control information may enable determining areas traveled by vehicles that are not included in conventional maps, as well as vehicle actions associated with particular locations, such as places where vehicles park or make other maneuvers.

A projected display with improved visibility and understandability of a vehicle behavior. A vehicle projection device includes a projector that forms a projected image on a road surface; and a control unit. The control unit causes the projector to perform flashing projection of the projected image in a first period from start of projection, and causes the projector to perform sequential projection of the projected image in response to transition from the first period to a second period.

A computer system for generating a scenario to be run in a simulation environment for testing the behaviour of an autonomous vehicle, the computer system comprising: a rendering component configured to: generate display data for causing a display to render a graphical user interface comprising an image of a driving environment and one or more agents within the driving environment; a parameter generator configured to generate in memory a user-defined parameter set responsive to user input defining the parameter set; and an expression manager configured to store in memory a user-defined expression set, responsive to user input defining the expression set, wherein each expression of the expression set is a user-defined function of one or more parameters of the parameter set; and a scenario generator configured to record the scenario in a scenario database; wherein the graphical user interface is configured to provide multiple agent fields for controlling the behaviour of the one or more agents when the scenario is run in a simulation environment, wherein each agent field is modifiable to associate therewith either a parameter of the user-defined parameter set or an expression of the user-defined expression set; and wherein the recorded scenario comprises the driving environment, the one or more agents, the user-defined parameter set, the user-defined expression set, and any user-defined associations between (i) the multiple agent fields and the user-defined parameter set and (ii) the multiple agent fields and the user-defined expression set, wherein each parameter associated with an agent field is controllable to directly modify an agent behaviour, and each parameter that is included in expression associated with an agent field is controllable to indirectly modify an agent behaviour.

The invention provides a planned trajectory generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a state space and an action space of a vehicle; according to the potential collision risk and the action space, classifying the predicted trajectories of the side vehicle to obtain a risk trajectory set and a safety trajectory set; and through a partially observable Markov decision process, modeling is performed on a state space, an action space, a risk trajectory set and a safety trajectory set to obtain a reference trajectory. The decision planning process under the uncertainty of the side vehicle behavior is modeled as POMDP, so that the uncertain behavior of the side vehicle is fully predicted, and the driving safety of the vehicle is ensured. Besides, in consideration of too high dimensionality of a multi-traffic-participant prediction trajectory cluster in a complex scene and easy falling into dimensionality disaster of the POMDP model, the prediction trajectory of the side vehicle is classified into a risk trajectory set and a safe trajectory set through a potential collision risk, so that state space dimensionality reduction is realized, and decision planning efficiency and decision stability are effectively improved.

The invention relates to the technical field of intelligent parking management, and discloses a curb parking dynamic charging and fee evasion prevention and control method based on multi-dimensional data fusion, which comprises the following steps: a data fusion reconstruction step: receiving a video stream, sensing a parking space state and reporting data through mobile inspection, performing fusion processing through space-time alignment and a confidence decision algorithm, and obtaining a data fusion result; outputting a standard parking event sequence; a dynamic charging decision-making step of matching a charging strategy model including time-phased elastic rate parameters according to the time-space attribute of the event sequence, calculating the cost and verifying the parking rights and interests in real time, and generating a parking order; and a fee evasion risk real-time prevention and control step: constructing a vehicle behavior portrait feature vector based on the event sequence and the historical order, obtaining a real-time risk level through a pre-trained risk grading model, and triggering a cascade response disposal action according to the real-time risk level, including exit interception and recovery of a high-risk vehicle. According to the invention, the charging accuracy is improved, and the rate dynamic adjustment and the active and accurate prevention and control of the fee evasion behavior are realized.

The invention relates to the technical field of multi-modal time sequence, and discloses a traffic participant intention prediction method and device based on multi-modal time sequence attention, and the method comprises the steps: collecting multi-modal original traffic data in a traffic scene; preprocessing the multi-modal original traffic data to obtain standardized multi-modal data; performing feature extraction on the standardized multi-modal data to obtain a multi-dimensional feature sequence; performing dynamic weighted fusion and semantic association learning on the multi-dimensional feature sequence through a time sequence attention model to obtain a fused feature sequence; performing traffic participant intention probability prediction based on the fused feature sequence to obtain an intention prediction result containing an intention category and a corresponding confidence coefficient; and transmitting the intention prediction result to a decision planning module of the automatic driving system, wherein the decision planning module obtains a vehicle behavior decision strategy adaptive to the current traffic scene based on the intention category and the corresponding confidence coefficient.

The invention relates to an unmanned aerial vehicle threat intention prediction method and device and a storage medium, and is applied to the technical field of anti-unmanned aerial vehicles. The method specifically comprises the steps that through multi-modal deep feature fusion and deep adversarial learning, the system can extract deeper and more abstract unmanned aerial vehicle behavior mode features from multi-source heterogeneous data, so that accurate prediction of the real threat intention of the unmanned aerial vehicle is achieved, the limitation that a traditional method is only based on surface feature judgment is overcome, and the accuracy of the unmanned aerial vehicle threatening intention prediction is improved. The transformation of threat assessment from post-event analysis to pre-event prediction is realized; in the deep adversarial learning framework, various complex unmanned aerial vehicle threat behavior modes including disguise, interference and novel attack strategies can be simulated, and meanwhile, the recognition capability is continuously improved in adversarial training, so that the system can still keep high accuracy and low false alarm rate when facing unknown or variable threats, and the safety of the system is improved. And the robustness and adaptability of the system are obviously enhanced.

The vehicular state estimation apparatus includes: a physical quantity detector configured to detect a physical quantity indicating behavior by the vehicle; a vehicle behavior estimator configured to estimate current behavior by the vehicle; a distance sensor that is configured to detect a value pertaining to a distance between the vehicle body member and a measurement point on a road surface in front of the vehicle and corresponding to at least a central section of a road surface contact section on a wheel; a distance calculator configured to calculate a road surface distance; a spring constant calculator configured to calculate at least one of spring constants, in a vehicle stopped state; and a suspension controller configured to control operation of the active suspension apparatus based on the spring constants, the road surface distance, and vehicle behavior information.

The invention provides an expressway lane dynamic optimization management method, system and device and a medium, and belongs to the technical field of intelligent traffic management. The method comprises the following steps: firstly, collecting traffic flow, environment and vehicle credit data through high-speed key node multi-source sensing equipment and a data center, and fusing to generate a time-space fusion data set; inputting the data into a space-time diagram convolution network model, dynamically mapping a road topology into a variable weight directed graph, and generating future lane-level traffic flow, vehicle speed and occupancy rate prediction data through convolution operation; inputting the prediction data into a hierarchical reinforcement learning model, generating a single-vehicle differential management and control instruction in combination with vehicle credit data, and determining an optimal strategy through digital twinborn simulation; then, a modularized lane controller is used for executing a strategy and collecting feedback data; and finally, inputting feedback data into a causal inference engine to analyze the effect, optimizing the reinforcement learning model, and dynamically updating credit data according to vehicle behavior data.

Proposed are an apparatus for and a method of controlling a vehicle suspension. The apparatus includes a sensor configured to acquire at least one of information on a road surface in front of a vehicle and state information of the vehicle; and a processor configured to predict a vehicular behavior based on the information acquired through the sensor and actuator information and to control at least one of a ride height of the vehicle, stiffness of an air spring, and a damping force of a damper based on the predicted vehicular behavior.

The invention provides an intelligent traffic cone barrel anti-collision early warning method based on binocular vision, and aims to improve the safety of a road construction operation area. And accurate data are provided for subsequent detection and analysis through a binocular camera high-precision calibration technology based on circle detection. In combination with a data acquisition and enhancement technology, high-quality training data is provided for an improved RT-DETR algorithm, and the accuracy and efficiency of vehicle identification are significantly improved. A binocular vision depth perception and StrongSORT multi-target tracking algorithm is utilized to realize real-time speed measurement and distance measurement of a vehicle, and accurate input is provided for track prediction. Based on a GAN multi-modal trajectory prediction technology, a plurality of future trajectories are generated, and a multi-level early warning decision is realized through a comprehensive collision risk index (CRI). According to the method, the limitation of an existing prediction method in a complex road scene is effectively solved, the vehicle behavior intention is predicted and analyzed through the multi-modal trajectory, and reliable early warning information can be provided under various complex road conditions.

A system for infrastructure and environmental mapping and automated vehicle behavior modification includes a plurality of sensors of an autonomous vehicle and an autonomy computing system. The sensors capture sensor data representing an environment in which the autonomous vehicle is operating. The processor receives, from the sensors, first sensor data representing the environment in which the autonomous vehicle is operating at a first time, the first sensor data including image or video data. The processor also detects a difference between the first sensor data and historical sensor data at a location within the environment, and, based on the detected difference, stores an incident record indexed to the location relative to a stored map of the environment. When one or more adjustment criteria associated with the location are satisfied, the processor initiates one or more remedial actions associated with operation of the autonomous vehicle within the environment.