Design and Realization of a Benchmarking Testbed for Evaluating Autonomous Platooning Algorithms

• URL: http://arxiv.org/abs/2402.09233v1
• Date: Wed, 14 Feb 2024 15:22:24 GMT
• Title: Design and Realization of a Benchmarking Testbed for Evaluating Autonomous Platooning Algorithms
• Authors: Michael Shaham, Risha Ranjan, Engin Kirda, Taskin Padir
• Abstract summary: This paper introduces a testbed for evaluating and benchmarking platooning algorithms on 1/10th scale vehicles with onboard sensors. We evaluate three algorithms, linear feedback and two variations of distributed model predictive control, and compare their results on a typical platooning scenario. We find that the distributed model predictive control algorithms outperform linear feedback on hardware and in simulation.
• Score: 8.440060524215378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous vehicle platoons present near- and long-term opportunities to enhance operational efficiencies and save lives. The past 30 years have seen rapid development in the autonomous driving space, enabling new technologies that will alleviate the strain placed on human drivers and reduce vehicle emissions. This paper introduces a testbed for evaluating and benchmarking platooning algorithms on 1/10th scale vehicles with onboard sensors. To demonstrate the testbed's utility, we evaluate three algorithms, linear feedback and two variations of distributed model predictive control, and compare their results on a typical platooning scenario where the lead vehicle tracks a reference trajectory that changes speed multiple times. We validate our algorithms in simulation to analyze the performance as the platoon size increases, and find that the distributed model predictive control algorithms outperform linear feedback on hardware and in simulation.

Related papers

• Learning a Stable, Safe, Distributed Feedback Controller for a Heterogeneous Platoon of Vehicles [5.289123253466164]
  We introduce an algorithm for learning a stable, safe, distributed controller for a heterogeneous platoon. Our algorithm relies on recent developments in learning neural network stability and safety certificates.
  arXiv  Detail & Related papers  (2024-04-18T19:11:34Z)
• Modelling, Positioning, and Deep Reinforcement Learning Path Tracking Control of Scaled Robotic Vehicles: Design and Experimental Validation [3.807917169053206]
  Scaled robotic cars are commonly equipped with a hierarchical control acthiecture that includes tasks dedicated to vehicle state estimation and control. This paper covers both aspects by proposing (i) a federeted extended Kalman filter (FEKF) and (ii) a novel deep reinforcement learning (DRL) path tracking controller trained via an expert demonstrator. The experimentally validated model is used for (i) supporting the design of the FEKF and (ii) serving as a digital twin for training the proposed DRL-based path tracking algorithm.
  arXiv  Detail & Related papers  (2024-01-10T14:40:53Z)
• 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)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• 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)
• Collision-Free Navigation using Evolutionary Symmetrical Neural Networks [0.0]
  This paper extends the previous work using evolutionary neural networks for reactive collision avoidance. We are proposing a new method we have called symmetric neural networks. The method improves the model's performance by enforcing constraints between the network weights.
  arXiv  Detail & Related papers  (2022-03-29T13:02:14Z)
• Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
  We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
  arXiv  Detail & Related papers  (2021-09-16T13:10:27Z)
• Model-based Decision Making with Imagination for Autonomous Parking [50.41076449007115]
  The proposed algorithm consists of three parts: an imaginative model for anticipating results before parking, an improved rapid-exploring random tree (RRT) and a path smoothing module. Our algorithm is based on a real kinematic vehicle model; which makes it more suitable for algorithm application on real autonomous cars. In order to evaluate the algorithm's effectiveness, we have compared our algorithm with traditional RRT, within three different parking scenarios.
  arXiv  Detail & Related papers  (2021-08-25T18:24:34Z)
• Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning [52.2663102239029]
  We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle on idle-hailing platforms. Our approach learns ride-based state-value function using a batch training algorithm with deep value. We benchmark our algorithm with baselines in a ride-hailing simulation environment to demonstrate its superiority in improving income efficiency.
  arXiv  Detail & Related papers  (2021-03-08T05:34:05Z)
• Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning [0.4588028371034407]
  We present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning. The proposed pipeline was employed to train and deploy three distinct driving behavior models onto a simulated vehicle.
  arXiv  Detail & Related papers  (2020-10-09T19:03:15Z)
• Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction [88.0416857308144]
  We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps. We directly simulate the outputs of the self-driving vehicle's perception and prediction system, enabling realistic motion planning testing.
  arXiv  Detail & Related papers  (2020-08-13T17:20:02Z)

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.