High Speed multi-vehicle Autonomous Racing will increase the safety and performance of road-going Autonomous Vehicles. Precise vehicle detection and dynamics estimation from a moving platform is a key requirement for planning and executing complex autonomous overtaking maneuvers. To address this requirement, we have developed a Latency-Aware EKF-based Multi Target Tracking algorithm fusing LiDAR and RADAR measurements. The algorithm explots the different sensor characteristics by explicitly integrating the Range Rate in the EKF Measurement Function, as well as a-priori knowledge of the racetrack during state prediction. It can handle Out-Of-Sequence Measurements via Reprocessing using a double State and Measurement Buffer, ensuring sensor delay compensation with no information loss. This algorithm has been implemented on Team PoliMOVE's autonomous racecar, and was proved experimentally by completing a number of fully autonomous overtaking maneuvers at speeds up to 275 km/h.

Autonomous vehicles have shown promising potential to be a groundbreaking technology for improving the safety of road users. For these vehicles, as well as many other safety-critical robotic technologies, to be deployed in real-world applications, we require algorithms that can generalize well to unseen scenarios and data. Model-based reinforcement learning algorithms (MBRL) have demonstrated state-of-the-art performance and data efficiency across a diverse set of domains. However, these algorithms have also shown susceptibility to changes in the environment and its transition dynamics. In this work, we explore the performance and generalization capabilities of MBRL algorithms for autonomous driving, specifically in the simulated autonomous racing environment, Roboracer (formerly F1Tenth). We frame the head-to-head racing task as a learning problem using contextual Markov decision processes and parameterize the driving behavior of the adversaries using the context of the episode, thereby also parameterizing the transition and reward dynamics. We benchmark the behavior of MBRL algorithms in this environment and propose a novel context-aware extension of the existing literature, cMask. We demonstrate that context-aware MBRL algorithms generalize better to out-of-distribution adversary behaviors relative to context-free approaches. We also demonstrate that cMask displays strong generalization capabilities, as well as further performance improvement relative to other context-aware MBRL approaches when racing against adversaries with in-distribution behaviors.

Abstract-- Autonomous racing requires tight integration between perception, planning and control to minimize latency as well as timely decision making. A standard autonomy pipeline comprising of a global planner, local planner, and controller loses information as the higher-level racing context is sequentially propagated downstream into specific task-oriented context. In particular, the global planner's understanding of optimality is typically reduced to a sparse set of waypoints, leaving the local planner to make reactive decisions with limited context. This paper investigates whether additional global insights, specifically time-optimality information, can be meaningfully passed to the local planner to improve downstream decisions. We introduce a framework that preserves essential global knowledge and convey it to the local planner through QuayPoints - regions where deviations from the optimal raceline result in significant compromises to optimality. QuayPoints enable local planners to make more informed global decisions when deviating from the raceline, such as during strategic overtaking. T o demonstrate this, we integrate QuayPoints into an existing planner and show that it consistently overtakes opponents traveling at up to 75% of the ego vehicle's speed across four distinct race tracks.

Sampling-based trajectory planners are widely used for agile autonomous driving due to their ability to generate fast, smooth, and kinodynamically feasible trajectories. However, their behavior is often governed by a cost function with manually tuned, static weights, which forces a tactical compromise that is suboptimal across the wide range of scenarios encountered in a race. To address this shortcoming, we propose using a Reinforcement Learning (RL) agent as a high-level behavioral selector that dynamically switches the cost function parameters of an analytical, low-level trajectory planner during runtime. We show the effectiveness of our approach in simulation in an autonomous racing environment where our RL-based planner achieved 0% collision rate while reducing overtaking time by up to 60% compared to state-of-the-art static planners. Our new agent now dynamically switches between aggressive and conservative behaviors, enabling interactive maneuvers unattainable with static configurations. These results demonstrate that integrating reinforcement learning as a high-level selector resolves the inherent trade-off between safety and competitiveness in autonomous racing planners. The proposed methodology offers a pathway toward adaptive yet interpretable motion planning for broader autonomous driving applications.

Overtaking in high-speed autonomous racing demands precise, real-time estimation of collision risk; particularly in wheel-to-wheel scenarios where safety margins are minimal. Existing methods for collision risk estimation either rely on simplified geometric approximations, like bounding circles, or perform Monte Carlo sampling which leads to overly conservative motion planning behavior at racing speeds. We introduce the Gauss-Legendre Rectangle (GLR) algorithm, a principled two-stage integration method that estimates collision risk by combining Gauss-Legendre with a non-homogeneous Poisson process over time. GLR produces accurate risk estimates that account for vehicle geometry and trajectory uncertainty. In experiments across 446 overtaking scenarios in a high-fidelity Formula One racing simulation, GLR outperforms five state-of-the-art baselines achieving an average error reduction of 77% and surpassing the next-best method by 52%, all while running at 1000 Hz. The framework is general and applicable to broader motion planning contexts beyond autonomous racing.

Abstract--A significant challenge in autonomous racing is to generate overtaking maneuvers. Racing agents must execute these maneuvers on complex racetracks with little room for error . Optimization techniques and graph-based methods have been proposed, but these methods often rely on oversimplified assumptions for collision-avoidance and dynamic constraints. In this work, we present an approach to trajectory synthesis based on an extension of the Differential Bayesian Filtering framework. Our method is derivative-free, does not require a spherical approximation of the vehicle footprint, linearization of constraints, or simplifying upper bounds on collision avoidance. We conduct a closed-loop analysis of DBF-MA and find it successfully overtakes an opponent in 87% of tested scenarios, outperforming existing methods in autonomous overtaking. Autonomous racing has emerged as a distinct and growing research area [1].

F1Tenth is a widely adopted reduced-scale platform for developing and testing autonomous racing algorithms, hosting annual competitions worldwide. With high operating speeds, dynamic environments, and head-to-head interactions, autonomous racing requires algorithms that diverge from those in classical autonomous driving. Training such algorithms is particularly challenging: the need for rapid decision-making at high speeds severely limits model capacity. To address this, we propose End2Race, a novel end-to-end imitation learning algorithm designed for head-to-head autonomous racing. End2Race leverages a Gated Recurrent Unit (GRU) architecture to capture continuous temporal dependencies, enabling both short-term responsiveness and long-term strategic planning. We also adopt a sigmoid-based normalization function that transforms raw LiDAR scans into spatial pressure tokens, facilitating effective model training and convergence. The algorithm is extremely efficient, achieving an inference time of less than 0.5 milliseconds on a consumer-class GPU. Experiments in the F1Tenth simulator demonstrate that End2Race achieves a 94.2% safety rate across 2,400 overtaking scenarios, each with an 8-second time limit, and successfully completes overtakes in 59.2% of cases. This surpasses previous methods and establishes ours as a leading solution for the F1Tenth racing testbed. Code is available at https://github.com/michigan-traffic-lab/End2Race.

In autonomous racing, reactive controllers eliminate the computational burden of the full See-Think-Act autonomy stack by directly mapping sensor inputs to control actions. This bypasses the need for explicit localization and trajectory planning. A widely adopted baseline in this category is the Follow-The-Gap method, which performs trajectory planning using LiDAR data. Building on FTG, the Delaunay Triangulation-based Racing algorithm introduces further enhancements. However, DTR's use of circumcircles for trajectory generation often results in insufficiently smooth paths, ultimately degrading performance. Additionally, the commonly used F1TENTH-simulator for autonomous racing competitions lacks support for 3D LiDAR perception, limiting its effectiveness in realistic testing. To address these challenges, this work proposes the MCTR algorithm. MCTR improves trajectory smoothness through the use of Curvature Corrected Moving Average and implements a digital twin system within the CARLA simulator to validate the algorithm's robustness under 3D LiDAR perception. The proposed algorithm has been thoroughly validated through both simulation and real-world vehicle experiments.