Papers Autonomous Racing
“Autonomous Racing” 태그가 달린 논문 86편 · 필터 해제
Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flow
Accurate velocity estimation is critical in mobile robotics, particularly for driver assistance systems and autonomous driving. Wheel odometry fused with Inertial Measurement Unit (IMU) data is a widely used method for v…
Autonomous DrivingAutonomous RacingEvent-based Optical FlowOptical Flow EstimationLongitudinal Control for Autonomous Racing with Combustion Engine Vehicles
Usually, a controller for path- or trajectory tracking is employed in autonomous driving. Typically, these controllers generate high-level commands like longitudinal acceleration or force. However, vehicles with combusti…
Autonomous DrivingAutonomous RacingA Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7
Deep reinforcement learning has achieved superhuman racing performance in high-fidelity simulators like Gran Turismo 7 (GT7). It typically utilizes global features that require instrumentation external to a car, such as …
Autonomous RacingDeep Reinforcement LearningLow-pass sampling in Model Predictive Path Integral Control
Model Predictive Path Integral (MPPI) control is a widely used sampling-based approach for real-time control, offering flexibility in handling arbitrary dynamics and cost functions. However, the original MPPI suffers fro…
Autonomous RacingPerformance-driven Constrained Optimal Auto-Tuner for MPC
A key challenge in tuning Model Predictive Control (MPC) cost function parameters is to ensure that the system performance stays consistently above a certain threshold. To address this challenge, we propose a novel metho…
Autonomous RacingBayesian OptimizationModel Predictive ControlA Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing
Guaranteeing constraint satisfaction is challenging in imitation learning (IL), particularly in tasks that require operating near a system's handling limits. Traditional IL methods often struggle to enforce constraints, …
Autonomous RacingImitation LearningGeneralizable Image Repair for Robust Visual Autonomous Racing
Vision-based autonomous racing relies on accurate perception for robust control. However, image distribution changes caused by sensor noise, adverse weather, and dynamic lighting can degrade perception, leading to subopt…
Autonomous RacingDomain AdaptationImage-to-Image TranslationGame Theory in Formula 1: Multi-agent Physical and Strategical Interactions
This paper presents an optimization framework to model Formula 1 racing dynamics, where multiple cars interact physically and strategically. Aerodynamic wake effects, trajectory optimization, and energy management are in…
Autonomous Racingenergy managementManagementRaceVLA: VLA-based Racing Drone Navigation with Human-like Behaviour
RaceVLA presents an innovative approach for autonomous racing drone navigation by leveraging Visual-Language-Action (VLA) to emulate human-like behavior. This research explores the integration of advanced algorithms that…
Autonomous RacingDrone navigationFair Play in the Fast Lane: Integrating Sportsmanship into Autonomous Racing Systems
Autonomous racing has gained significant attention as a platform for high-speed decision-making and motion control. While existing methods primarily focus on trajectory planning and overtaking strategies, the role of spo…
Autonomous RacingDecision MakingFairnessTrajectory PlanningExAMPC: the Data-Driven Explainable and Approximate NMPC with Physical Insights
Amidst the surge in the use of Artificial Intelligence (AI) for control purposes, classical and model-based control methods maintain their popularity due to their transparency and deterministic nature. However, advanced …
Autonomous RacingModel Predictive ControlSymbolic RegressionA Racing Dataset and Baseline Model for Track Detection in Autonomous Racing
A significant challenge in racing-related research is the lack of publicly available datasets containing raw images with corresponding annotations for the downstream task. In this paper, we introduce RoRaTrack, a novel d…
Autonomous RacingGenerative Adversarial NetworkLong-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing
With the expansion of autonomous driving technology, autonomous racing has been actively studied in recent years. For safe autonomous racing, fast computation speed and wide detection range are essential. However, existi…
Autonomous DrivingAutonomous RacingClusteringComputational Efficiency+2RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms
Autonomous racing presents a complex environment requiring robust controllers capable of making rapid decisions under dynamic conditions. While traditional controllers based on tire models are reliable, they often demand…
Autonomous RacingReinforcement Learning (RL)LiDAR-Based Vehicle Detection and Tracking for Autonomous Racing
Autonomous racing provides a controlled environment for testing the software and hardware of autonomous vehicles operating at their performance limits. Competitive interactions between multiple autonomous racecars howeve…
Autonomous RacingAutonomous VehiclesComputational EfficiencyPoint Cloud Segmentation+3Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows
Importance sampling is a rare event simulation technique used in Monte Carlo simulations to bias the sampling distribution towards the rare event of interest. By assigning appropriate weights to sampled points, importanc…
Autonomous RacingCollision AvoidanceParallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous Racing
Autonomous driving in high-speed racing, as opposed to urban environments, presents significant challenges in scene understanding due to rapid changes in the track environment. Traditional sequential network approaches m…
Autonomous DrivingAutonomous RacingScene UnderstandingMulti-Task Reinforcement Learning for Quadrotors
Reinforcement learning (RL) has shown great effectiveness in quadrotor control, enabling specialized policies to develop even human-champion-level performance in single-task scenarios. However, these specialized policies…
Autonomous Racingreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Self-Driving Car Racing: Application of Deep Reinforcement Learning
This paper explores the application of deep reinforcement learning (RL) techniques in the domain of autonomous self-driving car racing. Motivated by the rise of AI-driven mobility and autonomous racing events, the projec…
AI AgentAutonomous DrivingAutonomous RacingCar Racing+6Learn 2 Rage: Experiencing The Emotional Roller Coaster That Is Reinforcement Learning
This work presents the experiments and solution outline for our teams winning submission in the Learn To Race Autonomous Racing Virtual Challenge 2022 hosted by AIcrowd. The objective of the Learn-to-Race competition is …
Autonomous DrivingAutonomous RacingReinforcement Learning (RL)