Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to the field of advanced driver assistance systems (ADAS) and autonomous vehicle technologies. More specifically, it pertains to a context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks.
BACKGROUND OF THE DISCLOSURE
[0002] The evolution of road transportation has undergone significant transformation in recent decades due to rapid advances in sensing technologies, artificial intelligence, and vehicle automation. The demand for improved road safety, driver assistance, and traffic efficiency has stimulated extensive research and industrial developments in advanced driver-assistance systems (ADAS). These systems aim to support drivers in controlling vehicles by providing warnings, automation in specific driving tasks, and, in some cases, partial or full control of vehicle operations. While the ultimate vision is the realization of fully autonomous vehicles, ADAS represents a crucial transitional step in bridging conventional human-driven vehicles with autonomous driving technologies.
[0003] Early efforts in driver assistance primarily focused on mechanical improvements and basic alerting mechanisms, such as anti-lock braking systems, cruise control, and lane departure warnings. These systems, though effective, operated primarily on predefined rules and lacked the ability to adapt to highly dynamic and uncertain real-world road conditions. As road networks became increasingly congested, the limitations of such deterministic systems became apparent. This led to the integration of computational intelligence, sensors, and communication modules to enable more context-sensitive assistance to drivers.
[0004] One of the most critical challenges in this domain is trajectory planning, which involves predicting and determining the optimal path a vehicle should follow given its surroundings, the state of the driver, and the nature of traffic dynamics. Traditional trajectory planning methods, often rooted in kinematic models and rule-based algorithms, were effective under structured and predictable road conditions. However, their performance degraded in scenarios involving high uncertainty, such as intersections, unstructured roads, occlusions, and interactions with vulnerable road users like pedestrians and cyclists. These limitations sparked interest in adaptive and intelligent approaches capable of accounting for environmental variability and multi-agent interactions.
[0005] The introduction of multi-modal sensors marked a major milestone in the evolution of ADAS. Initially, vehicles relied heavily on radar for adaptive cruise control and collision avoidance due to its robustness in various weather conditions. Cameras were subsequently introduced to provide visual information necessary for tasks such as lane keeping, traffic sign recognition, and pedestrian detection. LiDAR further enhanced the perception stack by enabling high-resolution 3D mapping of the environment. Ultrasonic sensors added close-range detection capabilities, often used in parking assistance. The fusion of these sensors created a more comprehensive environmental model by compensating for the weaknesses of individual modalities. For instance, radar excels in long-range detection but lacks detailed classification capabilities, while cameras offer rich semantic understanding but are sensitive to lighting conditions. LiDAR provides high accuracy but is expensive and sensitive to adverse weather. Multi-modal sensor fusion thus emerged as a critical research focus to ensure robust and reliable perception under diverse scenarios.
[0006] Trajectory planning is inherently tied to perception because decisions on how the vehicle should move depend on an accurate and timely understanding of the surroundings. Early research on trajectory planning often employed geometric and optimization-based methods. These approaches formulated the trajectory as an optimization problem with constraints derived from vehicle dynamics, safety margins, and road geometry. Algorithms such as Rapidly Exploring Random Trees (RRT), A\, and model predictive control (MPC) became popular for generating feasible trajectories. While these methods were mathematically sound and offered some real-time applicability, their dependence on precise environmental models limited their effectiveness in uncertain or rapidly changing scenarios. Moreover, optimization-based methods were often computationally intensive, which posed challenges for real-time implementation in vehicles with limited processing capacity.
[0007] As machine learning gained prominence, researchers began to explore data-driven approaches to trajectory prediction and planning. Supervised learning methods were applied to predict driver intentions or future positions of vehicles using historical trajectory datasets. These models showed promise in capturing complex patterns of behavior that were difficult to encode explicitly. However, they were often limited by the availability of labeled data and lacked the ability to generalize to unseen situations. Reinforcement learning (RL) approaches also gained traction, where agents learned driving policies through trial and error in simulated environments. RL demonstrated the potential to generate adaptive and risk-sensitive driving strategies, though transferring such policies from simulation to real-world driving remained a substantial challenge.
[0008] The importance of context-awareness in trajectory planning became evident as vehicles encountered heterogeneous environments with varying traffic rules, cultural driving behaviors, and situational risks. For example, driving strategies appropriate for highways may not be suitable for dense urban areas with frequent pedestrian crossings. Context-awareness in ADAS refers to the ability of the system to interpret external conditions such as weather, traffic density, road type, and internal states such as driver fatigue or distraction. Traditional ADAS systems often operated without integrating such contextual information, leading to suboptimal decisions. Incorporating contextual cues allows trajectory planning systems to better anticipate risks, adapt driving strategies, and ensure safety while maintaining efficiency.
[0009] Another dimension of ADAS development involves risk assessment. Risk is an inherent aspect of driving, and the ability to quantify and adapt to it is fundamental for safe trajectory planning. Early risk models relied on deterministic safety distances, time-to-collision calculations, and braking distances. While simple to compute, these models failed to capture complex interactions between multiple road users and did not adequately account for uncertainty in sensor measurements. Probabilistic risk assessment methods were introduced to model uncertainty explicitly, leveraging Bayesian inference, Gaussian processes, and stochastic modeling. These methods allowed for more nuanced predictions but often required significant computational resources. The emergence of neural networks offered an alternative by enabling data-driven risk modeling, where systems could learn implicit risk factors from large-scale driving datasets.
[0010] The integration of neural networks into trajectory planning further revolutionized ADAS capabilities. Convolutional neural networks (CNNs) excelled in extracting spatial features from sensor data, particularly camera and LiDAR inputs. Recurrent neural networks (RNNs) and their variants, such as LSTM and GRU, were applied to capture temporal dependencies in trajectories and motion patterns. More recently, transformer-based architectures have been explored for their ability to model long-range dependencies and multi-agent interactions more effectively. Neural networks offered flexibility, scalability, and adaptability, making them well-suited for highly dynamic driving environments.
[0011] Despite significant progress, several challenges persist in the field of ADAS trajectory planning. First, the problem of sensor noise and redundancy remains unresolved. Even with sensor fusion, issues such as misalignment, calibration errors, and environmental distortions introduce uncertainty into perception data. Second, real-time processing is critical in driving scenarios, and computationally heavy models risk latency that could compromise safety. Third, ethical considerations and explainability of AI-driven decisions are vital, especially in risk-sensitive applications like driving. Regulatory frameworks and industry standards increasingly demand transparency in how ADAS systems make decisions, which is challenging given the black-box nature of many neural network models.
[0012] Another pressing issue is the variability of driving behavior across different regions, cultures, and road conditions. A trajectory planning system trained on data from one region may not perform optimally in another, where traffic dynamics and behavioral norms differ. Domain adaptation and transfer learning techniques have been explored to address this challenge, but further research is needed to ensure global applicability of ADAS technologies. Moreover, interaction with human drivers poses unique challenges for partially automated vehicles. Unlike fully autonomous systems, ADAS must coexist with and assist human drivers, which requires understanding driver intentions, preferences, and trust levels. Context-aware and risk-adaptive approaches hold promise in addressing these human-machine interaction issues.
[0013] Standardization and benchmarking of ADAS technologies also remain a concern. While various research initiatives and automotive companies have developed trajectory planning algorithms, there is no universally accepted framework for evaluating their performance across different contexts. Simulations provide valuable insights but may fail to capture the full complexity of real-world driving. Large-scale field testing is expensive and time-consuming. As a result, the adoption of advanced trajectory planning systems has been uneven across regions and manufacturers.
[0014] The advent of connected and cooperative intelligent transportation systems (C-ITS) introduces new opportunities and complexities for trajectory planning. With vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, ADAS can access richer information beyond onboard sensors. For instance, vehicles can exchange intentions, positions, and velocity data, allowing more coordinated and predictive trajectory planning. However, reliance on connectivity introduces cybersecurity risks and requires robust data integrity mechanisms. The fusion of multi-modal sensor data with connected information further increases the computational and algorithmic complexity of trajectory planning systems.
[0015] Research communities have increasingly acknowledged the need for integrated approaches that combine the strengths of traditional planning methods, machine learning, and contextual reasoning. Hybrid systems, where model-based algorithms provide safety guarantees while data-driven models offer adaptability, are emerging as a promising direction. Risk-adaptive neural networks, capable of adjusting their predictions and strategies based on perceived risk levels, represent one such advancement. By dynamically balancing aggressiveness and conservatism in driving strategies, such systems aim to ensure both safety and efficiency.
[0016] The societal and economic implications of advanced ADAS are profound. On the societal front, road traffic accidents remain a major cause of injuries and fatalities worldwide. According to global statistics, human error accounts for the majority of road accidents. By reducing driver workload, preventing errors, and offering proactive assistance, trajectory planning systems contribute directly to enhancing road safety. On the economic side, improved driving efficiency reduces fuel consumption and emissions, aligning with sustainability goals. Moreover, advanced ADAS technologies enhance the competitiveness of automotive manufacturers and pave the way for broader adoption of autonomous vehicles.
[0017] The regulatory landscape has been evolving to keep pace with technological developments in ADAS. Different regions have introduced varying levels of mandates for driver-assistance features, such as emergency braking, lane keeping assistance, and collision avoidance. Trajectory planning, being a core element of ADAS, indirectly influences compliance with these regulations. At the same time, policymakers face the challenge of balancing innovation with safety, ensuring that new systems undergo rigorous testing and validation before deployment. This regulatory push has encouraged both academia and industry to focus on developing robust, explainable, and context-aware trajectory planning methods.
[0018] Thus, in light of the above-stated discussion, there exists a need for a context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks.
SUMMARY OF THE DISCLOSURE
[0019] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0020] According to illustrative embodiments, the present disclosure focuses on a context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0021] An objective of the present disclosure is to implement risk-adaptive neural networks that dynamically adjust trajectory generation strategies based on the perceived level of risk and uncertainty in the environment.
[0022] Another objective of the present disclosure is to develop a real-time trajectory planning framework capable of generating safe, smooth, and efficient driving paths in complex and dynamic environments such as dense urban traffic, adverse weather, or unexpected road obstacles.
[0023] Another objective of the present disclosure is to design a multi-modal sensor fusion architecture that integrates heterogeneous inputs from LiDAR, radar, cameras, GPS, and inertial measurement units (IMUs) to create a robust, comprehensive perception of the driving scene.
[0024] Another objective of the present disclosure is to incorporate contextual awareness into trajectory planning by modeling dynamic risk factors such as pedestrian behavior, driver intention, vehicle interactions, and environmental conditions.
[0025] Another objective of the present disclosure is to achieve faster adaptation to rapidly changing scenarios by combining predictive modeling of surrounding agents with real-time feedback control, ensuring safe maneuvers in unpredictable traffic conditions.
[0026] Another objective of the present disclosure is to enhance passenger comfort and driving experience by producing trajectories that balance safety, efficiency, and ride smoothness, reducing abrupt braking, sharp turns, or erratic accelerations.
[0027] Another objective of the present disclosure is to reduce system dependency on static rule-based decision making by leveraging machine learning-driven planning that generalizes to diverse road layouts, traffic behaviors, and environmental conditions.
[0028] Another objective of the present disclosure is to establish a hierarchical decision-making pipeline that combines long-term global route planning with short-term local trajectory optimization, enabling both proactive and reactive driving capabilities.
[0029] Another objective of the present disclosure is to ensure scalability and robustness of the system across different vehicle platforms and ADAS functionalities, ranging from lane keeping and adaptive cruise control to autonomous emergency maneuvers.
[0030] Yet another objective of the present disclosure is to validate the proposed system through simulations and real-world testing under diverse driving conditions, ensuring compliance with safety standards, regulatory requirements, and user trust in ADAS technology.
[0031] In light of the above, a context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks comprises a multi-modal sensor fusion module configured to receive and integrate data from a plurality of heterogeneous sensors. The system also includes a contextual perception unit configured to incorporate vehicle dynamics data and environmental parameters into the fused sensor data to provide situational awareness. The system also includes a deep neural network-based trajectory planning module trained on diverse driving scenarios. The system also includes a risk-adaptive decision module configured to assess real-time threats. The system also includes a driver behavior profiling module configured to personalize trajectory generation based on individual driving styles. The system also includes an explainable AI module configured to provide transparent reasoning behind trajectory decisions for regulatory compliance and interpretability. The system also includes a real-time optimization module configured to continuously update and adapt generated trajectories to accommodate lane changes, dynamic object motion, and varying traffic conditions.
[0032] In one embodiment, the multi-modal sensor fusion module further comprises a probabilistic weighting mechanism configured to resolve inconsistencies or conflicts between heterogeneous sensor inputs.
[0033] In one embodiment, the contextual perception unit is further configured to incorporate weather data, road topology, and traffic signal information to enhance situational awareness.
[0034] In one embodiment, the deep neural network-based trajectory planning module comprises a hybrid architecture integrating convolutional neural networks and transformer-based models to jointly capture spatial and temporal dependencies.
[0035] In one embodiment, the risk-adaptive decision module dynamically assigns risk scores to environmental entities based on their proximity, velocity, and predicted trajectory relative to the host vehicle.
[0036] In one embodiment, the risk-adaptive decision module further comprises a reinforcement learning framework configured to improve threat assessment accuracy through continuous exposure to diverse driving scenarios.
[0037] In one embodiment, the explainable AI module provides human-interpretable justifications for trajectory decisions using feature attribution methods including saliency maps and attention weight visualization.
[0038] In one embodiment, the explainable AI module is further configured to generate regulatory-compliant logs documenting risk factors, decision pathways, and trajectory outcomes.
[0039] In one embodiment, the real-time optimization module utilizes model predictive control techniques to continuously refine generated trajectories under changing environmental and vehicle dynamics constraints.
[0040] In one embodiment, the real-time optimization module is further configured to perform computational load balancing across on-board processors to maintain low-latency performance in edge computing environments.
[0041] These and other advantages will be apparent from the present application of the embodiments described herein.
[0042] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0043] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0044] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0045] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0046] FIG. 1 illustrates a flowchart outlining sequential step involved in a context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks, in accordance with an exemplary embodiment of the present disclosure;
[0047] FIG. 2 illustrates a flowchart showing working of a context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks, in accordance with an exemplary embodiment of the present disclosure.
[0048] Like reference, numerals refer to like parts throughout the description of several views of the drawing;
[0049] The context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0050] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0051] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0052] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0053] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0054] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0055] Referring now to FIG. 1 to FIG. 2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a flowchart outlining sequential step involved in a context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks, in accordance with an exemplary embodiment of the present disclosure.
[0056] A context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks 100 comprises a multi-modal sensor fusion module 102 configured to receive and integrate data from a plurality of heterogeneous sensors. The multi-modal sensor fusion module 102 further comprises a probabilistic weighting mechanism configured to resolve inconsistencies or conflicts between heterogeneous sensor inputs.
[0057] The system also includes a contextual perception unit 104 configured to incorporate vehicle dynamics data and environmental parameters into the fused sensor data to provide situational awareness. The contextual perception unit 104 is further configured to incorporate weather data, road topology, and traffic signal information to enhance situational awareness.
[0058] The system also includes a deep neural network-based trajectory planning module 106 trained on diverse driving scenarios. The deep neural network-based trajectory planning module 106 comprises a hybrid architecture integrating convolutional neural networks and transformer-based models to jointly capture spatial and temporal dependencies.
[0059] The system also includes a risk-adaptive decision module 108 configured to assess real-time threats. The risk-adaptive decision module 108 dynamically assigns risk scores to environmental entities based on their proximity, velocity, and predicted trajectory relative to the host vehicle. The risk-adaptive decision module 108 further comprises a reinforcement learning framework configured to improve threat assessment accuracy through continuous exposure to diverse driving scenarios.
[0060] The system also includes a driver behavior profiling module 110 configured to personalize trajectory generation based on individual driving styles.
[0061] The system also includes an explainable AI module 112 configured to provide transparent reasoning behind trajectory decisions for regulatory compliance and interpretability. The explainable AI module 112 provides human-interpretable justifications for trajectory decisions using feature attribution methods including saliency maps and attention weight visualization. The explainable AI module 112 is further configured to generate regulatory-compliant logs documenting risk factors, decision pathways, and trajectory outcomes.
[0062] The system also includes a real-time optimization module 114 configured to continuously update and adapt generated trajectories to accommodate lane changes, dynamic object motion, and varying traffic conditions. The real-time optimization module 114 utilizes model predictive control techniques to continuously refine generated trajectories under changing environmental and vehicle dynamics constraints. The real-time optimization module 114 is further configured to perform computational load balancing across on-board processors to maintain low-latency performance in edge computing environments.
[0063] FIG. 1 illustrates a flowchart outlining sequential step involved in a context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks.
[0064] At 102, the multi-modal sensor fusion module receives continuous input streams from different sensing modalities such as cameras, LiDAR, radar, and GPS. Each sensor contributes unique information the camera provides visual cues such as lane markings and traffic lights, LiDAR captures three-dimensional spatial geometry of the environment, radar detects object velocities and distances under diverse weather conditions, and GPS supplies global positioning information. The fusion of these heterogeneous data sources creates a unified and comprehensive representation of the surrounding environment, reducing blind spots and increasing robustness against sensor-specific limitations.
[0065] At 104, once the fused sensory representation is available, it is passed into the contextual perception unit. At this stage, the system augments the scene information with vehicle dynamics data such as steering angle, acceleration, braking force, and velocity, along with environmental parameters such as road type, weather conditions, and illumination levels. By embedding these contextual factors, the system develops situational awareness, ensuring that trajectory planning is not performed solely on raw sensor perception but also on the operational context of the vehicle. This integration is crucial in distinguishing between normal driving conditions and exceptional states, such as wet roads or sharp curves, which significantly affect maneuvering choices.
[0066] At 106, the enhanced contextual data is then processed by the deep neural network-based trajectory planning module. This module has been trained on diverse real-world and simulated driving scenarios to generate feasible and safe paths for the vehicle. Within the neural network, spatiotemporal attention layers play a pivotal role, selectively focusing on the most relevant features of the input at each time step. For instance, in heavy traffic, the model prioritizes nearby vehicle trajectories, while in suburban environments it may focus more on pedestrian crossings. The attention mechanism ensures that computational resources are effectively utilized and that the trajectory planning remains sensitive to dynamic environmental changes. The output from this stage is an initial trajectory proposal that reflects both local structural constraints and global route considerations.
[0067] At 108, before this trajectory is finalized, the system activates the risk-adaptive decision module. This module continuously evaluates the environment for potential threats such as sudden pedestrian entry onto the road, unexpected braking by leading vehicles, slippery road surfaces, or reduced visibility in foggy conditions. Using probabilistic threat models and real-time data streams, the module recalibrates the trajectory whenever risk levels exceed predefined thresholds. For example, if the road ahead is icy, the system reduces aggressive maneuvers and opts for smoother, lower-acceleration paths. If a pedestrian is detected stepping into the crosswalk, the system dynamically adjusts the trajectory to slow down or reroute safely. This risk-aware adjustment ensures proactive rather than reactive decision-making, enhancing safety margins.
[0068] At 110, parallel to risk assessment, the driver behavior profiling module influences the planning process by embedding personalization into the trajectory generation. The system maintains profiles of driving styles aggressive drivers may prefer quicker overtakes and sharper lane changes, while cautious drivers may prioritize smoother turns and larger safety distances. By adapting trajectory decisions to the driver’s preferred style, the system ensures that the assistance feels natural and acceptable, increasing user trust and reducing the likelihood of overrides. Over time, the profiling module learns and adapts to subtle behavioral changes, refining personalization dynamically.
[0069] At 112, the trajectory, once adapted for both environmental risks and driver behavior, is then passed through the explainable AI module. This module provides human-interpretable reasoning behind each decision made by the trajectory planner. For example, if the system decides to slow down at an unusual location, it can explain that radar detected a fast-approaching vehicle from a blind spot or that LiDAR identified a road debris hazard. This transparency is essential for regulatory compliance and for building user confidence in automated decision-making. By making the black box nature of deep learning interpretable, the explainable AI module closes the gap between advanced algorithmic processing and human understanding.
[0070] At 114, the real-time optimization module ensures that the planned trajectories are not static but evolve continuously to accommodate lane changes, dynamic object movements, and shifting traffic conditions. The module recalculates the path in milliseconds, ensuring responsiveness to immediate changes in the driving environment. For instance, if another vehicle abruptly cuts into the lane, the system recalibrates to maintain safe spacing. If a green traffic signal turns yellow, the system adjusts acceleration profiles accordingly. Importantly, the optimization is performed on edge hardware within the vehicle, ensuring low-latency operation suitable for real-time ADAS deployment. This edge-level computation minimizes dependence on external connectivity, making the system resilient in areas with poor network coverage.
[0071] FIG. 2 illustrates a flowchart showing working of a context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks.
[0072] At the top of the framework lies the input layer, which collects real-time data from a variety of sensors, including LiDAR, cameras, radar, GPS, and vehicle telemetry systems that provide speed and acceleration details. These inputs serve as the foundation of the system, ensuring that the vehicle is constantly aware of its surrounding environment, movement status, and positioning. This continuous influx of data forms the basis for subsequent processing and decision-making.
[0073] The first major processing block is the Environmental Scene Interpretation module. This stage focuses on transforming raw sensor data into meaningful environmental information by conducting object detection, semantic segmentation, and road topology extraction. By identifying and classifying objects, recognizing lane markings and road boundaries, and mapping road layouts, this module provides the autonomous system with a contextual understanding of the driving environment. It effectively enables the vehicle to see and interpret its surroundings much like a human driver would, but in a highly precise and computationally structured manner.
[0074] Following this, the Driver Profile and Risk Assessment stage evaluates the current driving context by analyzing driver tendencies and environmental conditions. The system categorizes driving behavior into profiles such as aggressive, defensive, or normal, while also accounting for the condition of the road and potential hazards. By integrating these insights, the system is able to adapt decision-making processes according to both environmental risks and expected driver style, ensuring greater safety and personalization of vehicle control strategies.
[0075] Next comes the Spatiotemporal Feature Extraction module, where an attention-based neural network is used to identify dynamic and context-aware features. This stage captures not only static objects but also time-dependent interactions, such as the movement of pedestrians or the changing behavior of surrounding vehicles. By learning these spatiotemporal patterns, the system enhances its situational awareness and prepares richer inputs for trajectory planning.
[0076] The central block of the framework is the Trajectory Generation Module. At this stage, a deep neural network generates multiple trajectory options for the vehicle, each optimized for critical factors such as safety, comfort, and operational efficiency. Rather than committing to a single path immediately, the system considers a range of possibilities to ensure robust decision-making under varying conditions. These paths account for potential obstacles, speed requirements, and long-term driving objectives.
[0077] Once candidate paths are generated, the Risk-Adaptive Path Selection module is responsible for choosing the most appropriate one. This decision is guided by scoring mechanisms that weigh risk factors, traffic rules, and the driver’s profile. By incorporating adaptive reasoning, the system ensures that the selected trajectory balances efficiency and safety while maintaining compliance with legal and contextual constraints. This stage emphasizes adaptability, tailoring choices to both external conditions and internal system priorities.
[0078] The Explainability Layer follows, serving a crucial role in transparency and trust. It provides reasoning behind the chosen path by highlighting key decision factors such as obstacle avoidance strategies or speed management. This interpretability layer is particularly important for debugging, validation, and fostering user confidence in the autonomous driving system, as it allows stakeholders to understand why specific decisions were made.
[0079] Finally, the Real-Time Execution module implements the selected path by sending precise commands to vehicle control systems. This includes steering, acceleration, and braking actions that bring the chosen trajectory to life. At the same time, feedback is continuously monitored to ensure the vehicle remains aligned with the plan and to allow for rapid adjustments in case of sudden changes in the environment. This closing loop ensures safe, adaptive, and reliable execution of the driving plan.
[0080] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0081] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0082] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0083] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0084] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A context-aware trajectory planning system for ADAS using multi-modal sensor fusion and risk-adaptive neural networks (100) comprising:
a multi-modal sensor fusion module (102) configured to receive and integrate data from a plurality of heterogeneous sensors;
a contextual perception unit (104) configured to incorporate vehicle dynamics data and environmental parameters into the fused sensor data to provide situational awareness;
a deep neural network-based trajectory planning module (106) trained on diverse driving scenarios;
a risk-adaptive decision module (108) configured to assess real-time threats;
a driver behavior profiling module (110) configured to personalize trajectory generation based on individual driving styles;
an explainable AI module (112) configured to provide transparent reasoning behind trajectory decisions for regulatory compliance and interpretability;
a real-time optimization module (114) configured to continuously update and adapt generated trajectories to accommodate lane changes, dynamic object motion, and varying traffic conditions.
2. The system (100) as claimed in claim 1, wherein the multi-modal sensor fusion module (102) further comprises a probabilistic weighting mechanism configured to resolve inconsistencies or conflicts between heterogeneous sensor inputs.
3. The system (100) as claimed in claim 1, wherein the contextual perception unit (104) is further configured to incorporate weather data, road topology, and traffic signal information to enhance situational awareness.
4. The system (100) as claimed in claim 1, wherein the deep neural network-based trajectory planning module (106) comprises a hybrid architecture integrating convolutional neural networks and transformer-based models to jointly capture spatial and temporal dependencies.
5. The system (100) as claimed in claim 1, wherein the risk-adaptive decision module (108) dynamically assigns risk scores to environmental entities based on their proximity, velocity, and predicted trajectory relative to the host vehicle.
6. The system (100) as claimed in claim 1, wherein the risk-adaptive decision module (108) further comprises a reinforcement learning framework configured to improve threat assessment accuracy through continuous exposure to diverse driving scenarios.
7. The system (100) as claimed in claim 1, wherein the explainable AI module (112) provides human-interpretable justifications for trajectory decisions using feature attribution methods including saliency maps and attention weight visualization.
8. The system (100) as claimed in claim 1, wherein the explainable AI module (112) is further configured to generate regulatory-compliant logs documenting risk factors, decision pathways, and trajectory outcomes.
9. The system (100) as claimed in claim 1, wherein the real-time optimization module (114) utilizes model predictive control techniques to continuously refine generated trajectories under changing environmental and vehicle dynamics constraints.
10. The system (100) as claimed in claim 1, wherein the real-time optimization module (114) is further configured to perform computational load balancing across on-board processors to maintain low-latency performance in edge computing environments.