System for dynamic arrival time estimation (ETA) and route optimization in autonomous vehicles using contextual geospatial intelligence

Abstract

A system (100) for dynamic estimated time of arrival (ETA) and route optimization in autonomous vehicles using contextual spatial intelligence, comprising: (a) a real-time data collection and integration module configured to receive and integrate real-time data from external sources, including traffic, weather, road conditions and on-board vehicle sensors; (b) a contextual geospatial information module configured to process the integrated data and interpret contextual information, including road closures, accidents, weather conditions and other dynamic environmental factors; (c) a dynamic route optimization algorithm module configured to evaluate multiple potential routes based on real-time data and calculate the most optimal route by applying optimization algorithms; (d) an estimated time of arrival (ETA) prediction and adjustment module configured to calculate and continuously update the estimated time of arrival (ETA) of the vehicle in real time and to adjust it based on changes to the optimised route and environmental factors; (e) a vehicle communication and coordination module configured to facilitate communication between the vehicle, other vehicles and infrastructure components to exchange and receive real-time data regarding traffic, accidents and route conditions; and (f) a feedback and learning module configured to collect feedback on the vehicle's journey, including route success, ETA prediction accuracy and real-time changes, and employ machine learning techniques to improve the system's route optimization and decision-making algorithms.

Description

[0001] The present invention relates to a system for dynamic estimated time of arrival (ETA) estimation and route optimization in autonomous vehicles. It uses contextual geospatial intelligence to evaluate real-time data such as traffic, weather, and road conditions to adapt routes and improve travel time accuracy. The system improves the autonomy and efficiency of vehicle navigation by making optimal route decisions based on constantly changing environmental factors.

[0002] Autonomous vehicles are making rapid progress, but optimizing their navigation while taking into account real-time changes in traffic, road conditions, weather, and other environmental factors remains a major challenge. Current route planning systems often rely on static maps or predefined algorithms that fail to account for the dynamics of real-world conditions, leading to inaccurate estimated times of arrival (ETA) and suboptimal route decisions. Therefore, there is an urgent need for an advanced system that can intelligently adapt to this constantly changing data to improve vehicle autonomy and driving efficiency.

[0003] The problem lies in the difficulty of incorporating contextual, real-time geospatial information into existing autonomous vehicle navigation systems. While conventional methods such as GPS-based route planning enable basic navigation, they often overlook local changes such as road closures, accidents, or adverse weather conditions that can drastically impact travel time. This leads to inaccurate arrival times, delays, and inefficient routes, which can reduce the overall performance of autonomous vehicles, increase fuel consumption, and impact user satisfaction.

[0004] The proposed system solves these problems by integrating real-time data sources, including traffic information, weather updates, and sensor inputs from the vehicle's surroundings, into a dynamic route optimization system. Leveraging advanced algorithms and contextual geospatial information, the system continuously analyzes the vehicle's route and adapts it to real-time conditions. This approach ensures that the autonomous vehicle can make informed decisions by dynamically adjusting its route and estimated time of arrival to optimize travel time, reduce delays, and improve the overall efficiency and safety of vehicle operations.

[0005] One objective of this disclosure is to continuously update the estimated time of arrival based on real-time data to ensure more accurate arrival times.

[0006] Another object of the present disclosure is the dynamic adjustment of routes to avoid traffic, accident and weather disruptions, thereby improving travel efficiency.

[0007] Another objective of the present disclosure is to respond immediately to changing road and environmental conditions, thereby improving navigation performance.

[0008] Another objective of the present disclosure is to enable autonomous vehicles to make decisions in real time without human intervention.

[0009] Another objective of the present disclosure is to reduce unnecessary detours and idle times, resulting in lower fuel consumption or energy consumption.

[0010] Another objective of the present disclosure is to utilize vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to improve overall traffic flow.

[0011] Another objective of the present disclosure is to facilitate the efficient management of large fleets by optimizing routes for multiple vehicles simultaneously.

[0012] Another subject of the present disclosure is machine learning, which continuously refines the route optimization and ETA prediction algorithms for better future performance.

[0013] The present invention relates to a system for dynamically calculating and optimizing estimated times of arrival (ETA) and routes for autonomous vehicles. It uses real-time data from traffic, weather, and vehicle sensors to adapt routes and arrival times to changing conditions, thus ensuring optimal travel efficiency.

[0014] Another embodiment of the present invention is for the system to include a real-time data acquisition and integration module that continuously collects data from external sources such as traffic management systems, weather reports, and vehicle sensors. This ensures that up-to-date information is always incorporated into the vehicle's navigation decisions.

[0015] Another embodiment of the present invention is the Context-Aware Geospatial Intelligence Module, which analyzes real-time data to understand environmental changes such as accidents, road closures, and weather disruptions. It uses machine learning to predict changes in traffic behavior and adjusts the vehicle's navigation plan accordingly.

[0016] Another embodiment of the present invention is the dynamic route optimization algorithm, which evaluates multiple possible routes in real time based on current conditions. By applying advanced optimization techniques, the system selects the most efficient route while adapting to changes such as road closures or accidents.

[0017] In another embodiment of the present invention, the ETA prediction and adjustment module continuously recalculates the vehicle's estimated time of arrival, taking into account real-time route adjustments. It ensures that the estimated time of arrival remains accurate by considering environmental factors that affect travel time during the trip.

[0018] Another embodiment of the present invention is that the system enables V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communication through a vehicle communication and coordination module. This collaboration helps the vehicle exchange real-time traffic information and optimize routes based on collective data from nearby vehicles and infrastructure.

[0019] Another embodiment of the present invention is the system's feedback and learning module, which collects data from previous trips, including route performance and arrival time accuracy. Machine learning algorithms are used to refine the system's predictive models and continuously improve route optimization over time.

[0020] The proposed invention introduces a dynamic ETA and route optimization system for autonomous vehicles that leverages contextual geoinformation to improve navigation efficiency and adaptability. This system consists of multiple interconnected modules that collectively provide real-time route adjustments, improved decision-making, and precise ETA calculations.

[0021] The Real-Time Data Collection and Integration module serves as the base component and continuously collects data from a variety of sources, including traffic information, weather forecasts, road conditions, in-vehicle sensor data, and spatial data from the surrounding environment. The module connects to external data sources such as traffic monitoring systems and weather APIs and integrates this information with the vehicle's sensors (e.g., LIDAR, radar, and cameras). This data is fed into the system in real time and forms the basis for intelligent decision-making.

[0022] The Context-Aware Geospatial Intelligence Module is responsible for processing the incoming data to interpret and understand the real-time context in which the vehicle is traveling. This module uses advanced machine learning algorithms to analyze environmental factors such as construction sites, accidents, and roadblocks that cannot be immediately detected or predicted by conventional systems. It then correlates these contextual changes with the vehicle's current position, historical route patterns, and expected traffic behavior, creating a dynamic understanding of the vehicle's operating environment.

[0023] The Dynamic Route Optimization Algorithm module leverages insights from the context-aware system and continuously re-evaluates the vehicle's route. It simulates multiple potential routes in real time, taking into account current traffic conditions, weather, road closures, and vehicle-specific data (e.g., fuel level or battery charge for electric vehicles). Using optimization techniques such as shortest-path algorithms and predictive models based on machine learning, this module calculates the best route and dynamically adapts to unexpected changes to ensure the fastest and most efficient path is taken.

[0024] The Estimated Time of Arrival (ETA) Prediction and Adjustment module works with the Route Optimization module to update and refine the vehicle's estimated time of arrival (ETA). Unlike static ETA models, this module considers the vehicle's adjustments in real time and recalculates the ETA based on the newly optimized route and any interruptions that may occur during the journey. This ensures that the system delivers an accurate and constantly updated estimated time of arrival, allowing passengers and other systems (such as fleet management or logistics software) to plan accordingly.

[0025] The Vehicle Communication and Coordination module enables the autonomous vehicle to communicate and coordinate with other vehicles and infrastructure components in its environment. Using vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication protocols, this module enables the system to share real-time data on traffic conditions, accidents, and other dynamic factors, improving the collaborative optimization of routes within a larger network of autonomous vehicles. This module contributes to reducing congestion, avoiding bottlenecks, and improving overall traffic flow, benefiting both individual vehicles and the entire traffic ecosystem.

[0026] Finally, the feedback and learning module enables the system to continuously improve its performance over time. This module uses feedback from the vehicle's journey, such as driver behavior, route success rate, and the accuracy of real-world ETA predictions, to optimize the system's algorithms. It applies machine learning techniques to adapt and optimize decision-making processes, ensuring the system becomes more efficient and accurate as it encounters a wider range of scenarios and environmental conditions.

[0027] Together, these modules form a comprehensive, intelligent and adaptable system that increases vehicle autonomy, improves arrival time accuracy and ensures that the most efficient and safest routes are always chosen, even in complex, dynamic environments.

[0028] The invention is explained again below with reference to the figure, which shows: Fig. : an illustration of a system for dynamic ETA and route optimization in autonomous vehicles using context-aware geospatial intelligence.

[0029] Fig.shows an illustration of a system for dynamic ETA and route optimization in autonomous vehicles using context-aware spatial intelligence. The system operates by first collecting real-time data through the Real-Time Data Acquisition and Integration module, which gathers inputs from external traffic data, weather data, and vehicle sensors. This data is processed by the Context-Aware Geospatial Intelligence Module, which interprets the environmental context and analyzes factors such as road closures, accidents, and weather conditions to provide a dynamic understanding of the vehicle's environment. The Dynamic Route Optimization Algorithm module then takes this context data and continuously recalculates the optimal route by simulating multiple options and selecting the best path based on real-time conditions.At the same time, the ETA prediction and adjustment module updates the estimated time of arrival (ETA) in real time, taking into account any route changes or delays. The vehicle communication and coordination module enables the vehicle to exchange and receive data with other vehicles and infrastructure, enabling collaborative route optimization and improved traffic management. Finally, the feedback and learning module collects driving data to continuously improve system performance and optimize algorithms based on past experience and real-world data. This integrated operation ensures that the vehicle adapts to changing conditions, improving efficiency, safety, and accuracy throughout the journey.

Claims

[1] A system (100) for dynamic estimated time of arrival (ETA) and route optimization in autonomous vehicles using contextual spatial intelligence, comprising: (a) a real-time data collection and integration module configured to receive and integrate real-time data from external sources, including traffic, weather, road conditions and on-board vehicle sensors; (b) a contextual geospatial information module configured to process the integrated data and interpret contextual information, including road closures, accidents, weather conditions and other dynamic environmental factors; (c) a dynamic route optimization algorithm module configured to evaluate multiple potential routes based on real-time data and calculate the most optimal route by applying optimization algorithms; (d) an estimated time of arrival (ETA) prediction and adjustment module configured to calculate and continuously update the estimated time of arrival (ETA) of the vehicle in real time and to adjust it based on changes to the optimised route and environmental factors; (e) a vehicle communication and coordination module configured to facilitate communication between the vehicle, other vehicles and infrastructure components to exchange and receive real-time data regarding traffic, accidents and route conditions; and (f) a feedback and learning module configured to collect feedback on the vehicle's journey, including route success, ETA prediction accuracy and real-time changes, and employ machine learning techniques to improve the system's route optimization and decision-making algorithms. [2] The system (100) of claim 1, wherein the real-time data collection and integration module further comprises a connection to traffic management systems, weather forecast services, and live road condition monitoring systems. [3] The system (100) of claim 1, wherein the context-aware geospatial intelligence module uses machine learning algorithms to predict real-time changes in traffic patterns, road conditions, and weather events based on historical data and current environmental conditions. [4] The system (100) of claim 1, wherein the dynamic route optimization algorithm module is configured to dynamically recalculate the route of the vehicle during travel based on new data to ensure that the optimal route is selected at any time. [5] The system (100) of claim 1, wherein the ETA prediction and adjustment module includes a prediction model that incorporates vehicle-specific factors, such as fuel level or battery charge, into the ETA calculations. [6] The system (100) of claim 1, wherein the vehicle communication and coordination module uses vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication protocols to exchange and receive real-time traffic, accident, and road condition data. [7] The system (100) of claim 1, wherein the feedback and learning module further uses data from the vehicle's historical trips to refine and adapt the route optimization algorithms over time, thereby improving the predictive capabilities of the system. [8] The system (100) of claim 1, wherein the system provides continuous updates to a central server or fleet management system enabling remote monitoring and tracking of route optimization and estimated time of arrival for a fleet of autonomous vehicles.

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