Infrastructure-based End-to-End Learning and Prevention of Driver Failure

• URL: http://arxiv.org/abs/2303.12224v1
• Date: Tue, 21 Mar 2023 22:55:51 GMT
• Title: Infrastructure-based End-to-End Learning and Prevention of Driver Failure
• Authors: Noam Buckman, Shiva Sreeram, Mathias Lechner, Yutong Ban, Ramin Hasani, Sertac Karaman, Daniela Rus
• Abstract summary: FailureNet is a recurrent neural network trained end-to-end on trajectories of both nominal and reckless drivers in a scaled miniature city. It can accurately identify control failures, upstream perception errors, and speeding drivers, distinguishing them from nominal driving. Compared to speed or frequency-based predictors, FailureNet's recurrent neural network structure provides improved predictive power, yielding upwards of 84% accuracy when deployed on hardware.
• Score: 68.0478623315416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent intersection managers can improve safety by detecting dangerous drivers or failure modes in autonomous vehicles, warning oncoming vehicles as they approach an intersection. In this work, we present FailureNet, a recurrent neural network trained end-to-end on trajectories of both nominal and reckless drivers in a scaled miniature city. FailureNet observes the poses of vehicles as they approach an intersection and detects whether a failure is present in the autonomy stack, warning cross-traffic of potentially dangerous drivers. FailureNet can accurately identify control failures, upstream perception errors, and speeding drivers, distinguishing them from nominal driving. The network is trained and deployed with autonomous vehicles in the MiniCity. Compared to speed or frequency-based predictors, FailureNet's recurrent neural network structure provides improved predictive power, yielding upwards of 84% accuracy when deployed on hardware.

Related papers

• Enhancing Road Safety: Real-Time Detection of Driver Distraction through Convolutional Neural Networks [0.0]
  This study seeks to identify the most efficient model for real-time detection of driver distractions. The ultimate aim is to incorporate the findings into vehicle safety systems, significantly boosting their capability to prevent accidents triggered by inattention.
  arXiv  Detail & Related papers  (2024-05-28T03:34:55Z)
• Physical Backdoor Attack can Jeopardize Driving with Vision-Large-Language Models [53.701148276912406]
  Vision-Large-Language-models (VLMs) have great application prospects in autonomous driving. BadVLMDriver is the first backdoor attack against VLMs for autonomous driving that can be launched in practice using physical objects. BadVLMDriver achieves a 92% attack success rate in inducing a sudden acceleration when coming across a pedestrian holding a red balloon.
  arXiv  Detail & Related papers  (2024-04-19T14:40:38Z)
• FBLNet: FeedBack Loop Network for Driver Attention Prediction [75.83518507463226]
  Nonobjective driving experience is difficult to model. In this paper, we propose a FeedBack Loop Network (FBLNet) which attempts to model the driving experience accumulation procedure. Under the guidance of the incremental knowledge, our model fuses the CNN feature and Transformer feature that are extracted from the input image to predict driver attention.
  arXiv  Detail & Related papers  (2022-12-05T08:25:09Z)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• 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)
• Lane-GNN: Integrating GNN for Predicting Drivers' Lane Change Intention [5.23886447414886]
  We apply graph modelling on the traffic flow data generated by a popular mobility simulator, SUMO, at road segment levels. We then evaluate the performance of lane changing detection using the proposed Lane-GNN scheme. Our experimental results show that the proposed Lane-GNN can detect drivers' lane change intention within 90 seconds with an accuracy of 99.42%.
  arXiv  Detail & Related papers  (2022-07-02T12:53:56Z)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• Driving Anomaly Detection Using Conditional Generative Adversarial Network [26.45460503638333]
  This study proposes an unsupervised method to quantify driving anomalies using a conditional generative adversarial network (GAN) The approach predicts upcoming driving scenarios by conditioning the models on the previously observed signals. The results are validated with perceptual evaluations, where annotators are asked to assess the risk and familiarity of the videos detected with high anomaly scores.
  arXiv  Detail & Related papers  (2022-03-15T22:10:01Z)
• Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles [15.702721819948623]
  Existing testing methods are inadequate for checking the end-to-end behaviors of autonomous vehicles. We propose a new fuzz testing technique, called AutoFuzz, which can leverage widely-used AV simulators' API grammars. AutoFuzz efficiently finds hundreds of realistic traffic violations resembling real-world crashes.
  arXiv  Detail & Related papers  (2021-09-13T17:05:43Z)
• Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning [30.59728753059457]
  Traffic accident anticipation aims to predict accidents from dashcam videos as early as possible. Current deterministic deep neural networks could be overconfident in false predictions. We propose an uncertainty-based accident anticipation model with relational-temporal learning.
  arXiv  Detail & Related papers  (2020-08-01T20:21:48Z)

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.