Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles

• URL: http://arxiv.org/abs/2409.03114v2
• Date: Sun, 02 Mar 2025 15:30:06 GMT
• Title: Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles
• Authors: Beñat Froemming-Aldanondo, Tatiana Rastoskueva, Michael Evans, Marcial Machado, Anna Vadella, Rickey Johnson, Luis Escamilla, Milan Jostes, Devson Butani, Ryan Kaddis, Chan-Jin Chung, Joshua Siegel,
• Abstract summary: We evaluate five low-resource lane-following algorithms for real-time operation on vehicles with limited computing resources.<n>Top-performing methods used unsupervised learning to detect and separate lane lines with processing time under 10 ms per frame.<n>Findings highlight the potential for efficient lane detection approaches to enhance the accessibility and reliability of autonomous vehicle technologies.
• Score: 0.3179433314782644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable lane-following is essential for automated and assisted driving, yet existing solutions often rely on models that require extensive computational resources, limiting their deployment in compute-constrained vehicles. We evaluate five low-resource lane-following algorithms designed for real-time operation on vehicles with limited computing resources. Performance was assessed through simulation and deployment on real drive-by-wire electric vehicles, with evaluation metrics including reliability, comfort, speed, and adaptability. The top-performing methods used unsupervised learning to detect and separate lane lines with processing time under 10 ms per frame, outperforming compute-intensive and poor generalizing deep learning approaches. These approaches demonstrated robustness across lighting conditions, road textures, and lane geometries. The findings highlight the potential for efficient lane detection approaches to enhance the accessibility and reliability of autonomous vehicle technologies. Reducing computing requirements enables lane keeping to be widely deployed in vehicles as part of lower-level automation, including active safety systems.

Related papers

• TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning [61.33599727106222]
  TeLL-Drive is a hybrid framework that integrates a Teacher LLM to guide an attention-based Student DRL policy. A self-attention mechanism then fuses these strategies with the DRL agent's exploration, accelerating policy convergence and boosting robustness.
  arXiv  Detail & Related papers  (2025-02-03T14:22:03Z)
• Continual Learning for Behavior-based Driver Identification [5.560336593474866]
  Behavior-based Driver Identification is an emerging technology that recognizes drivers based on their unique driving behaviors. Most studies fail to account for the real-world challenges of deploying Deep Learning models within vehicles. These challenges include operating under limited computational resources, adapting to new drivers, and changes in driving behavior over time.
  arXiv  Detail & Related papers  (2024-12-14T10:24:44Z)
• Exploring the Causality of End-to-End Autonomous Driving [57.631400236930375]
  We propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving. Our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one.
  arXiv  Detail & Related papers  (2024-07-09T04:56:11Z)
• Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving [59.705635382104454]
  We present Bench2Drive, the first benchmark for evaluating E2E-AD systems' multiple abilities in a closed-loop manner. We implement state-of-the-art E2E-AD models and evaluate them in Bench2Drive, providing insights regarding current status and future directions.
  arXiv  Detail & Related papers  (2024-06-06T09:12:30Z)
• LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection [9.424132584616288]
  We propose a lightweight parallel neural network structure, LiPar, which achieve enhanced generalization capability for in-vehicle intrusion detection. LiPar adaptationally allocates task loads to in-vehicle computing devices, such as multiple electronic control units, domain controllers, computing gateways. We prove that LiPar has great detection performance, running efficiency, and lightweight model size, which can be well adapted to the in-vehicle environment practically and protect the in-vehicle CAN bus security.
  arXiv  Detail & Related papers  (2023-11-14T08:54:00Z)
• Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout [0.0]
  Connected and autonomous vehicles have the potential to minimize energy consumption. Existing deterministic and methods created to solve the eco-driving problem generally suffer from high computational and memory requirements. This work proposes a hierarchical multi-horizon optimization framework implemented via a neural network.
  arXiv  Detail & Related papers  (2023-10-16T23:13:51Z)
• Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation [0.24578723416255746]
  Reinforcement learning algorithms have emerged as a promising approach to address these challenges. Here, we address the problem of efficiently and safely navigating T-intersections using a lower-cost, single-agent approach. Our results reveal that the proposed approach enables the AV to effectively navigate T-intersections, outperforming previous methods in terms of travel delays, collision minimization, and overall cost.
  arXiv  Detail & Related papers  (2023-09-30T10:54:02Z)
• 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)
• End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data [6.849144123909844]
  This letter proposes the first fully self-supervised learning framework, self-supervised imitation learning (SSIL), for E2E driving. The proposed SSIL framework can learn E2E driving networks without using driving command data. Our numerical experiments with three different benchmark datasets demonstrate that the proposed SSIL framework achieves very comparable E2E driving accuracy with the supervised learning counterpart.
  arXiv  Detail & Related papers  (2023-08-28T06:17:15Z)
• Evaluation of Pedestrian Safety in a High-Fidelity Simulation Environment Framework [21.456269382916062]
  This paper proposes a pedestrian safety evaluation method for autonomous driving. We construct a high-fidelity simulation framework embedded with pedestrian safety-critical characteristics. The proposed simulation method and framework can be used to access different autonomous driving algorithms.
  arXiv  Detail & Related papers  (2022-10-17T03:53:50Z)
• Unified Automatic Control of Vehicular Systems with Reinforcement Learning [64.63619662693068]
  This article contributes a streamlined methodology for vehicular microsimulation. It discovers high performance control strategies with minimal manual design. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
  arXiv  Detail & Related papers  (2022-07-30T16:23:45Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• Scalable Vehicle Re-Identification via Self-Supervision [66.2562538902156]
  Vehicle Re-Identification is one of the key elements in city-scale vehicle analytics systems. Many state-of-the-art solutions for vehicle re-id mostly focus on improving the accuracy on existing re-id benchmarks and often ignore computational complexity. We propose a simple yet effective hybrid solution empowered by self-supervised training which only uses a single network during inference time.
  arXiv  Detail & Related papers  (2022-05-16T12:14:42Z)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)
• An Intelligent Self-driving Truck System For Highway Transportation [81.12838700312308]
  In this paper, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real-world deployment. We also deploy our proposed system on a real truck and conduct real world experiments which shows our system's capacity of mitigating sim-to-real gap.
  arXiv  Detail & Related papers  (2021-12-31T04:54:13Z)
• Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving [1.995792341399967]
  We develop a safe decision-making method for self-driving cars in a multi-lane, single-agent setting. The proposed approach utilizes deep reinforcement learning to achieve a high-level policy for safe tactical decision-making.
  arXiv  Detail & Related papers  (2021-05-13T19:17:30Z)
• Online and Adaptive Parking Availability Mapping: An Uncertainty-Aware Active Sensing Approach for Connected Vehicles [1.7259824817932292]
  We propose an online and adaptive scheme for parking availability mapping. Specifically, we adopt an information-seeking active sensing approach to select the incoming data, thus preserving the onboard storage and processing resources. We compare the proposed algorithm with several baselines, which attain inferior performance in terms of mapping convergence speed and adaptivity capabilities.
  arXiv  Detail & Related papers  (2021-05-01T13:35:36Z)
• Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images [58.287120077778205]
  We introduce a framework for analyzing robustness of the learning algorithm w.r.t varying quality in the image input for autonomous driving. Using the results of sensitivity analysis, we propose an algorithm to improve the overall performance of the task of "learning to steer"
  arXiv  Detail & Related papers  (2021-02-26T02:08:07Z)
• Vehicle Route Prediction through Multiple Sensors Data Fusion [0.0]
  The framework consists of two modules and both are working in sequence. The first module of our framework using a deep learning for recognizing the vehicle license plate number. The second module using supervised learning algorithm of machine learning for predicting the route of the vehicle.
  arXiv  Detail & Related papers  (2020-08-30T08:14:11Z)
• Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception Proposal [87.11988786121447]
  This paper presents a framework for 3D object detection and tracking for autonomous vehicles. The solution, based on a novel sensor fusion configuration, provides accurate and reliable road environment detection. A variety of tests of the system, deployed in an autonomous vehicle, have successfully assessed the suitability of the proposed perception stack.
  arXiv  Detail & Related papers  (2020-08-21T20:36:21Z)
• Multi-lane Detection Using Instance Segmentation and Attentive Voting [0.0]
  We propose a novel solution to multi-lane detection, which outperforms state of the art methods in terms of both accuracy and speed. We are able to obtain a lane segmentation accuracy of 99.87% running at 54.53 fps (average)
  arXiv  Detail & Related papers  (2020-01-01T16:48:42Z)

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.