The use of real-world motorsport telemetry data is limited by its relative rarity, commercial confidentiality and high cost, not to mention a lack of quantity when needed for comprehensive training of aero-stability prediction systems. A Denoising Diffusion Probabilistic Model (DDPM) framework which learns a joint statistical and temporal relationship between multi-channel vehicle sensor data and produces high fidelity training sequences indistinguishable from real telemetry from a race. It features a U-Net denoising backbone with cross channel temporal attention, a linear noise schedule over T = 1000 timesteps and a physics-informed Stability Score (?) consistency loss that allows to connect the generative process to the physics of aerodynamics. The results of extensive evaluation experiments on simulated Formula-like telemetry channels, covering channels speed, acceleration, suspension movement, wings load, tyre temperature, and aerodynamic pressure, show that the proposed diffusion model outperforms GAN, VAE, and LSTM synthesis baseline models on all three metrics: 96% downstream classifier accuracy, realism score of 0.94, and mean squared error between real and synthesized channels of 0.03. The framework is intended to be an underlying basis for aero-stability artificial intelligence training pipelines that need to work on scarce data.

Keywords : Motorsport Telemetry; Denoising Diffusion Probabilistic Models; Aero-Stability Prediction.

Authors : N Chandrika , Venkata Ramana Guntreddi

Title : Diffusion-Based Synthetic Motorsport Telemetry Generation for Aero-Stability Prediction Systems

Volume/Issue : 2024;1(1 ( October - December ))

Page No : 28 - 34

Vehicles with modern high performance run with complex multi-sensor data-streams under extreme aerodynamic and structural loading where there is a lack of accurate modelling in conventional linear state-space. This paper presents an extended Riemannian geometry framework for structural stability prediction in multi-sensor dynamic vehicle systems using an embedding of multi-sensor measurements onto a curved Riemannian manifold which better retains the true nature of the dynamic system behavior than the Euclidean distance assumption. The framework combines the synchronized acquisition of the sensors, projection of symmetric positive definite (SPD) covariance matrices for the manifolds, calculation of the geodesic distance, calculation of curvature using Christoffel symbols, calculation of a composite instability index (?), which is based on the curvature, geodesic divergence and velocity-weighted contributions. The system is run on an instrumented Formula type car at 1000 Hz on all the IMUs and 240 fps in all speed zones ranging from 80 to 340 km/h. Experimental comparison to the linear, Kalman filter and LSTM baselines shows that the proposed Riemannian framework achieves the highest structural state prediction accuracy of 96%, 3% higher than the LSTM baseline, the shortest prediction lead time (182 ms), and the lowest variance (? = 0.03) among the three approaches. The findings pave the way towards a mathematically robust, yet practically applicable basis for next generation structural health monitoring for safety-critical applications in vehicle structures.

Keywords : Structural Stability, Differential Geometry, Sensor Fusion, SPD Manifold, Instability Index.

Authors : Sk. Rukshana Begum , Mesfin Dagne

Title : Riemannian Geometry-Based Structural Stability Prediction in Dynamic Vehicle Systems

Volume/Issue : 2024;1(1 ( October - December ))

Page No : 21 - 27

Being a collaborative, iterative process, scientific discovery requires synthesis of large amounts of literature, the development of new theories or hypotheses, the design of new experiments, and extensive validation. Most of the existing automated research systems are centralized or single-agent pipelines whose ability is only applicable for parallel exploration, knowledge sharing, and consensus formation is limited. In this paper, a Collaborative Swarm Intelligence Framework (CSIF) for multi-agent scientific discovery is proposed where the rapidity and diversity of scientific discovery is accelerated by coordinating and sharing the knowledge of a swarm of specialized and autonomous research agents containing agents for literature search, hypothesis generation, experimental design, data analysis and validation. By relying on stigmergy with coordination and weighted consensus, it allows emergent collective intelligence, while avoiding any centralized bottlenecks as constructed from a swarm architecture. Novelty detection rates achieved are 98%, accuracy of the hypothesis of 96%, consensus quality score of 97% and the hypothesis validation classifier AUC of 0.96, significantly exceeding scores for single-agent, RAG-based, and hierarchical multi-agent baselines on experimental evaluation on arXiv metadata, Semantic Scholar corpora, as well as common scientific benchmark datasets.

Keywords : Swarm Intelligence, Multi-Agent Systems, Consensus Mechanisms, Hypothesis Generation.

Authors : C Nalini , Yam Krishna Poudel

Title : Collaborative Swarm Intelligence Framework for Multi-Agent Scientific Discovery Systems

Volume/Issue : 2024;1(1 ( October - December ))

Page No : 14 - 19

The ground-effect Formula 1 modern cars including today's are often aerodynamically unstable which are combined with aero-stall phenomenon and cause airflow separation. Prediction models that are only based on isolated telemetry reading and shallow learning models are not able to observe long-term temporal dependency of the measurement variable and short-term oscillatory behavior inherent in the high-speed aerodynamical environment. This paper presents a Hybrid Transformer–LSTM model to predict aero-stall generated in real-time, which combines Multi-head Self-attention with sequential memory learning by incorporating gated LSTMs. The proposed architecture implements sensor fusion with the following sensors: Inertial Measurement Units (IMU), Aerodynamic pressure sensors, Ride-height transducers and vibration accelerometers. To extract multiple-scale temporal features, Stepped Transformer layers are used as encoders, and LSTM memory blocks are used to store sequential evolution of the aerodynamic state. An Aero-Stall Index can be calculated to determine the level of degradation, thereby allowing one to work proactively on the race design. Experimental results show prediction accuracy of 98.5%, with a false alarm rate of 0.04 per hour and prediction lead time of 195 ms, this is superior to the other baselines CNN, standalone LSTM and Transformer on all metrics.

Keywords : Formula 1, Aero-Stall Prediction, Transformer, LSTM, Sensor Fusion, Deep Learning, Temporal

Authors : Nattala Subbarayudu , Ravindra Lal Weeraratne Koggalage

Title : Hybrid Transformer – LSTM Framework for Aero-Stall Prediction in Formula 1 Ground-Effect Vehicles

Volume/Issue : 2024;1(1 ( October - December ))

Page No : 8 - 13

Current motor sports environments can generate vast amounts of telemetry data from cameras, inertial measurement units (IMUs), pressure sensors, tire temperature sensors, ride-height sensors and vehicle dynamics systems. Traditional diagnostics systems can have processing lag, which can result in much longer reaction time under critical front-side time considerations. In this paper, a low latency sensor fusion architecture for motorsport real-time diagnostics with edge intelligence and multi-modal sensor signal fusion is proposed. The proposed architecture includes synchronised sensing, adaptive Kalman filtering, deep feature extraction and attention based predictive analytics with a very low end-to-end latency and significant diagnostic accuracy. The architecture features an attention-augmented TDN (Temporal Convolutional Network) trained on a 100% proprietary motorsport telemetry dataset which includes 48 race sessions and more than 27500 annotated fault instances in eight fault categories. Experimental results show that the proposed system processes the images with 84.4% faster time than the traditional cloud-based solution with only 28 ms of latency time on the edge platform (NVIDIA Jetson AGX Orin).Experimental evaluations demonstrate that a 98.6% accurate diagnostic tool can be executed using only 28 ms of latency time on an NVIDIA Jetson AGX Orin edge platform, which is an 84.4% reduction in latency time compared to traditional cloud-based approaches. With this system, the inference speed is 145 frames per second, which makes it possible to detect anomalies and predict faults in real-time when it comes to Formula 1 and endurance racing. The proposed architecture is shown to be superior in all dimensions of performance through comparison with the threshold-based, CNN fusion and Transformer baselines.

Keywords : Motorsport Diagnostics, Sensor Fusion, Edge AI, Temporal Convolutional Network, TensorRT.

Authors : N P Patnaik M

Title : Low-Latency Sensor Fusion Architecture for Real-Time Motorsport Diagnostics

Volume/Issue : 2024;1(1 ( October - December ))

Page No : 1-7