Core Viewpoint - The article emphasizes the importance of trajectory prediction in the context of autonomous driving and highlights the ongoing relevance of traditional two-stage and modular methods despite the rise of end-to-end approaches. It discusses the integration of trajectory prediction models with perception models as a form of end-to-end training, indicating a significant area of research and application in the industry [1][2]. Group 1: Trajectory Prediction Methods - The article introduces the concept of multi-agent trajectory prediction, which aims to forecast future movements based on the historical trajectories of multiple interacting agents. This is crucial for applications in autonomous driving, intelligent monitoring, and robotic navigation [1]. - It discusses the challenges of predicting human behavior due to its uncertainty and multimodality, noting that traditional methods often rely on recurrent neural networks, convolutional networks, or graph neural networks for social interaction modeling [1]. - The article highlights the advancements in diffusion models for trajectory prediction, showcasing models like Leapfrog Diffusion Model (LED) and Mixed Gaussian Flow (MGF) that have significantly improved accuracy and efficiency in various datasets [2]. Group 2: Course Objectives and Structure - The course aims to provide a systematic understanding of trajectory prediction and diffusion models, helping participants to integrate theoretical knowledge with practical coding skills, ultimately leading to the development of new models and research papers [6][8]. - It is designed for individuals at various academic levels who are interested in trajectory prediction and autonomous driving, offering insights into cutting-edge research and algorithm design [8]. - Participants will gain access to classic and cutting-edge papers, coding implementations, and methodologies for writing and submitting research papers [8][9]. Group 3: Course Highlights and Requirements - The course features a "2+1" teaching model with experienced instructors and dedicated support staff to enhance the learning experience [16][17]. - It requires participants to have a foundational understanding of deep learning and proficiency in Python and PyTorch, ensuring they can engage with the course material effectively [10]. - The course structure includes a comprehensive curriculum covering data sets, baseline codes, and essential research papers, facilitating a thorough understanding of trajectory prediction techniques [20][21][23].

Core Viewpoint - The article emphasizes the ongoing relevance and importance of trajectory prediction in autonomous driving, despite the rise of VLA (Vehicle Localization and Awareness) technologies. It highlights that trajectory prediction remains a critical module for ensuring safety and efficiency in driving systems [1][2]. Group 1: Trajectory Prediction Importance - Trajectory prediction is essential for autonomous driving systems as it helps in identifying potential hazards and planning optimal driving routes, thereby enhancing safety and efficiency [1]. - The quality of trajectory prediction directly impacts the planning and control of autonomous vehicles, making it a fundamental component of intelligent driving systems [1]. Group 2: Research and Development in Trajectory Prediction - Academic research in trajectory prediction is thriving, with significant focus on joint prediction, multi-agent prediction, and diffusion-based approaches, which are gaining traction in major conferences [1]. - The introduction of diffusion models has shown promise in improving multi-modal modeling capabilities for trajectory prediction, addressing the challenges posed by human behavior's uncertainty and multi-modality [2][3]. Group 3: Course Offering and Objectives - A new course on trajectory prediction using diffusion models is being offered, aimed at teaching research methods and paper publication strategies, particularly for multi-agent trajectory prediction [2][9]. - The course will cover various aspects, including classic and cutting-edge papers, baseline models, datasets, and writing methodologies, to help students develop a comprehensive understanding of the field [7][9]. Group 4: Course Structure and Content - The course spans 12 weeks of online group research followed by 2 weeks of paper guidance, with a focus on empirical validation using public datasets like ETH, UCY, and SDD [12][24]. - Key topics include the introduction of diffusion models, traditional trajectory prediction methods, and advanced techniques for integrating social interaction modeling and conditional control mechanisms [28][29].

Core Viewpoint - The article discusses the ongoing relevance of trajectory prediction in the context of end-to-end models, highlighting that many companies still utilize layered approaches where trajectory prediction remains a key algorithmic focus. This includes both joint trajectory prediction and target trajectory prediction, which continue to be active research areas with significant output in conferences and journals [1]. Group 1: Trajectory Prediction Research - The article emphasizes the importance of multi-agent trajectory prediction, which aims to forecast future movements based on historical trajectories of multiple interacting entities, crucial for applications in autonomous driving, intelligent monitoring, and robotic navigation [1]. - Traditional methods for trajectory prediction often rely on recurrent neural networks, convolutional networks, or graph neural networks, while generative models like GANs and CVAEs, although capable of simulating multimodal distributions, are noted for their inefficiency [1]. Group 2: Diffusion Models - Diffusion models have emerged as a new class of models that generate complex distributions through a stepwise denoising process, achieving significant breakthroughs in image generation and showing promise in trajectory prediction by enhancing multimodal modeling capabilities [2]. - Specific models such as the Leapfrog Diffusion Model (LED) and Mixed Gaussian Flow (MGF) have demonstrated substantial improvements in accuracy and efficiency, with LED achieving real-time predictions and MGF enhancing diversity in trajectory predictions [2]. Group 3: Course Objectives and Structure - The course aims to provide a systematic understanding of trajectory prediction and diffusion models, helping participants integrate theoretical knowledge with practical coding skills, and develop their own research ideas [6]. - Participants will gain insights into writing and submitting academic papers, with a focus on accumulating a methodology for writing and receiving guidance on revisions and submissions [6]. Group 4: Target Audience and Outcomes - The course is designed for graduate students and professionals in trajectory prediction and autonomous driving, aiming to enhance their resumes and research capabilities [8]. - Expected outcomes include a comprehensive understanding of classic and cutting-edge papers, coding implementations, and the development of a research paper draft [8][9]. Group 5: Course Highlights and Requirements - The course features a "2+1" teaching model with experienced instructors and a structured learning experience, ensuring comprehensive support throughout the research process [16][17]. - Participants are required to have a foundational understanding of deep learning and proficiency in Python and PyTorch, with recommendations for hardware specifications to facilitate learning [10][12].

Core Viewpoint - The article discusses the ongoing relevance of trajectory prediction in the context of end-to-end models, highlighting that many companies still utilize layered approaches where trajectory prediction remains a key algorithmic focus. The article emphasizes the significance of multi-agent trajectory prediction methods based on diffusion models, which are gaining traction in various applications such as autonomous driving and intelligent monitoring [1][2]. Group 1: Trajectory Prediction Research - Despite the rise of end-to-end models, trajectory prediction continues to be a hot research area, with significant output in conferences and journals [1]. - Multi-agent trajectory prediction aims to forecast future movements based on historical trajectories of multiple interacting agents, which is crucial in fields like autonomous driving and robotics [1]. - Traditional methods often struggle with the uncertainty and multimodality of human behavior, while generative models like GANs and CVAEs, although capable of simulating multimodal distributions, lack efficiency [1]. Group 2: Diffusion Models - Diffusion models have emerged as a new class of models that achieve complex distribution generation through gradual denoising, showing significant breakthroughs in image generation and other fields [2]. - The Leapfrog Diffusion Model (LED) enhances real-time prediction by reducing denoising steps, achieving a 19-30 times speedup while improving accuracy on various datasets [2]. - Mixed Gaussian Flow (MGF) and Pattern Memory-based Diffusion Model (MPMNet) are also highlighted for their advanced performance in trajectory prediction by better matching multimodal distributions and utilizing human motion patterns, respectively [2]. Group 3: Course Objectives and Structure - The course aims to provide a systematic understanding of trajectory prediction and diffusion models, helping students integrate theoretical knowledge with practical coding skills [6]. - It addresses common challenges faced by students, such as lack of direction and difficulties in reproducing research papers, by offering a structured approach to model development and academic writing [6]. - The course includes a comprehensive curriculum that covers classic and cutting-edge papers, coding implementations, and writing methodologies, ultimately guiding students to produce a draft of a research paper [6][9]. Group 4: Target Audience and Requirements - The course is designed for graduate students and professionals in trajectory prediction and autonomous driving, aiming to enhance their research capabilities and resume value [8]. - Participants are expected to have a foundational understanding of deep learning and familiarity with Python and PyTorch [10]. - The course emphasizes the importance of academic integrity and active participation, with specific requirements for attendance and assignment completion [15]. Group 5: Course Highlights and Outcomes - The program features a "2+1" teaching model with experienced instructors providing comprehensive support throughout the learning process [16][17]. - Students will gain access to datasets, baseline codes, and essential papers, facilitating a deeper understanding of the subject matter [20][21]. - Upon completion, students will have produced a research paper draft, a project completion certificate, and potentially a recommendation letter based on their performance [19].

Core Insights - The article presents a novel approach to trajectory prediction in autonomous driving, emphasizing a "First Reasoning, Then Forecasting" strategy that integrates intention reasoning to enhance prediction accuracy and reliability [2][4][48]. Group 1: Methodology - The proposed method introduces an intention reasoner based on a query-centric Inverse Reinforcement Learning (IRL) framework, which captures the behavior of traffic participants and their intentions in a compact representation [2][6][48]. - A bidirectional selective state space model (Bi-Mamba) is developed to improve trajectory decoding, effectively capturing the sequential dependencies of trajectory states [7][9][48]. - The framework utilizes a grid-level graph to represent the driving context, allowing for efficient modeling of participant behavior and intentions [5][6][20]. Group 2: Experimental Results - Extensive experiments on large datasets such as Argoverse and nuScenes demonstrate that the proposed method significantly enhances prediction confidence and achieves competitive performance compared to state-of-the-art models [9][34][38]. - In the Argoverse 1 dataset, the proposed method (FiM) outperformed several strong baseline methods in key metrics such as Brier score and minFDE6, indicating its robust predictive capabilities [34][35]. - The results from Argoverse 2 further validate the effectiveness of the intention reasoning strategy, showing that longer-term intention supervision improves prediction reliability [36][37]. Group 3: Challenges and Innovations - The article highlights the inherent challenges in modeling intentions due to the complexity of driving scenarios, advocating for the use of large reasoning models (LRMs) to enhance intention inference [5][6][12]. - The integration of a dense occupancy grid map (OGM) prediction head is introduced to model future interactions among participants, which enhances the overall prediction performance [7][25][41]. - The study emphasizes the importance of intention reasoning in motion prediction, establishing a promising baseline for future research in trajectory prediction [48].

Core Viewpoint - The article emphasizes the importance of social navigation capabilities in robots operating in complex environments, highlighting the challenges and advancements in this field [1][2]. Group 1: Social Navigation Challenges - Social navigation refers to robots executing navigation tasks while adhering to social norms in human-populated environments [2]. - Robots must avoid potential collisions and maintain appropriate social distances from humans, presenting unique challenges in visual navigation [5]. - Existing methods struggle with dynamic environments, as pre-built maps are inadequate for crowded spaces, and current reinforcement learning (RL) approaches face issues with short-sighted decision-making and reliance on global information [5] [9]. Group 2: Falcon Algorithm Introduction - A new algorithm named Falcon has been proposed by researchers from Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology [6]. - Falcon integrates trajectory prediction algorithms into social navigation tasks, enhancing long-term dynamic obstacle avoidance and navigation performance [7]. Group 3: Limitations of Existing Benchmarks - Current benchmarks for social navigation lack realism, often oversimplifying scenarios and failing to accurately represent human behavior [10][12]. - The research team has developed two new datasets, Social-HM3D and Social-MP3D, to address these limitations, providing a more realistic evaluation environment for social navigation tasks [10][23]. Group 4: Falcon Algorithm Components - The Falcon framework consists of two main modules: the Main Policy Network (MPN) and the Spatial-Temporal Precognition Module (SPM) [13][18]. - The MPN guides the robot's actions using a Social Cognition Penalty (SCP) mechanism to avoid interfering with human trajectories and maintain social distance [16]. - The SPM enhances the robot's ability to predict future environmental changes by combining trajectory prediction with various social perception tasks [17]. Group 5: Performance Evaluation - Falcon achieved a success rate of 55.15% and a success path length (SPL) in the Social-HM3D dataset, and a success rate of 55.05% in the untrained Social-MP3D dataset [29][30]. - The algorithm demonstrated nearly 90% personal space compliance and a collision rate of approximately 42% [31]. Group 6: Key Findings from Experiments - Future perception algorithms outperform traditional real-time perception methods, significantly enhancing safety and efficiency in dynamic environments [39][40]. - Auxiliary tasks, particularly trajectory prediction, are crucial for improving navigation performance, with success rates increasing from 40.94% to 54.00% when integrated [41][42]. - The combination of SCP and SPM improves performance and accelerates training convergence, with the complete Falcon model showing faster convergence and better overall performance [44][46].