Evaluation of Local Planner-Based Stanley Control in Autonomous RC Car Racing Series

• URL: http://arxiv.org/abs/2408.15152v1
• Date: Tue, 27 Aug 2024 15:50:31 GMT
• Title: Evaluation of Local Planner-Based Stanley Control in Autonomous RC Car Racing Series
• Authors: Máté Fazekas, Zalán Demeter, János Tóth, Ármin Bogár-Németh, Gergely Bári,
• Abstract summary: This paper proposes a control technique for autonomous RC car racing. It operates only local path planning on the actual LiDAR point cloud. It is tested on a 1/10-sized RC car, and the tuning procedure from a base solution to the optimal setting in a real F1Tenth race is presented.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a control technique for autonomous RC car racing. The presented method does not require any map-building phase beforehand since it operates only local path planning on the actual LiDAR point cloud. Racing control algorithms must have the capability to be optimized to the actual track layout for minimization of lap time. In the examined one, it is guaranteed with the improvement of the Stanley controller with additive control components to stabilize the movement in both low and high-speed ranges, and with the integration of an adaptive lookahead point to induce sharp and dynamic cornering for traveled distance reduction. The developed method is tested on a 1/10-sized RC car, and the tuning procedure from a base solution to the optimal setting in a real F1Tenth race is presented. Furthermore, the proposed method is evaluated with a comparison to a more simple reactive method, and in parallel to a more complex optimization-based technique that involves offline map building the global optimal trajectory calculation. The performance of the proposed method compared to the latter, referring to the lap time, is that the proposed one has only 8% lower average speed. This demonstrates that with appropriate tuning, a local planning-based method can be comparable with a more complex optimization-based one. Thus, the performance gap is lower than 10% from the state-of-the-art method. Moreover, the proposed technique has significantly higher similarity to real scenarios, therefore the results can be interesting in the context of automotive industry.

Related papers

• Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach [51.76826149868971]
  Policy evaluation via Monte Carlo simulation is at the core of many MC Reinforcement Learning (RL) algorithms. We propose as a quality index a surrogate of the mean squared error of a return estimator that uses trajectories of different lengths. We present an adaptive algorithm called Robust and Iterative Data collection strategy Optimization (RIDO)
  arXiv  Detail & Related papers  (2024-10-17T11:47:56Z)
• A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential [71.53284767149685]
  We present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar. We include a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation.
  arXiv  Detail & Related papers  (2023-12-22T16:29:55Z)
• Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles [3.200238632208686]
  Trajectory planning aims to produce a globally optimal route for Autonomous Passenger Vehicles. Existing implementations utilizing the vehicle bicycle kinematic model may not guarantee controllable trajectories. We augment this model by higher-order terms, including the first and second-order derivatives of curvature and longitudinal jerk.
  arXiv  Detail & Related papers  (2023-09-25T22:30:18Z)
• Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic [0.9281671380673306]
  This paper presents a parameterized bi-level optimization that jointly computes the optimal behavioural decisions and the resulting trajectory. Our approach runs in real-time using a custom GPU-accelerated batch, and a Variational Autoencoder learnt warm-start strategy. Our approach outperforms state-of-the-art model predictive control and RL approaches in terms of collision rate while being competitive in driving efficiency.
  arXiv  Detail & Related papers  (2022-12-05T12:56:42Z)
• A Data-Driven Slip Estimation Approach for Effective Braking Control under Varying Road Conditions [0.0]
  A novel estimation algorithm is proposed, based on a multi-layer neural network. The training is based on a synthetic data set, derived from a widely used friction model. The experimental results and the comparison with a model based baseline show that the proposed approach can provide an effective best slip estimation.
  arXiv  Detail & Related papers  (2022-11-04T16:24:05Z)
• Connection-Based Scheduling for Real-Time Intersection Control [6.796017024594715]
  We introduce a scheduling algorithm for real-time adaptive traffic signal control to reduce traffic congestion. This algorithm adopts a lane-based model that estimates the arrival time of all vehicles approaching an intersection through different lanes, and then computes a schedule that minimizes the cumulative delay incurred by all approaching vehicles.
  arXiv  Detail & Related papers  (2022-10-16T04:37:03Z)
• Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds [100.61456258283245]
  This paper presents a multi-layer motion planning and control architecture for autonomous racing. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
  arXiv  Detail & Related papers  (2022-07-22T15:16:54Z)
• Stress-Testing LiDAR Registration [52.24383388306149]
  We propose a method for selecting balanced registration sets, which are challenging sets of frame-pairs from LiDAR datasets. Perhaps unexpectedly, we find that the fastest and simultaneously most accurate approach is a version of advanced RANSAC.
  arXiv  Detail & Related papers  (2022-04-16T05:10:55Z)
• Autonomous Drone Racing with Deep Reinforcement Learning [39.757652701917166]
  In many robotic tasks, such as drone racing, the goal is to travel through a set of waypoints as fast as possible. A key challenge is planning the minimum-time trajectory, which is typically solved by assuming perfect knowledge of the waypoints to pass in advance. In this work, a new approach to minimum-time trajectory generation for quadrotors is presented.
  arXiv  Detail & Related papers  (2021-03-15T18:05:49Z)
• Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles [109.71532364079711]
  A new federated learning (FL) framework is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs) A novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, and the unbalanced and nonindependent and identically distributed data across CAVs. A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal controller.
  arXiv  Detail & Related papers  (2021-02-05T19:57:47Z)
• Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions [77.34726150561087]
  This paper investigates the use of reinforcement learning for the robust design of interplanetary trajectories in presence of severe disturbances. An open-source implementation of the state-of-the-art algorithm Proximal Policy Optimization is adopted. The resulting Guidance and Control Network provides both a robust nominal trajectory and the associated closed-loop guidance law.
  arXiv  Detail & Related papers  (2020-08-19T15:22:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.