CBANet: A Compact Attention-Based CNN-BiLSTM Network for Aggressive Driving Event Detection

• URL: http://arxiv.org/abs/2605.23471v1
• Date: Fri, 22 May 2026 10:30:28 GMT
• Title: CBANet: A Compact Attention-Based CNN-BiLSTM Network for Aggressive Driving Event Detection
• Abstract summary: Aggressive driving is a major cause of traffic accidents and poses a serious threat to road safety.<n>Deep learning methods have shown promising results in detecting risky driving behaviours from vehicle sensor data.<n>But their performance in real-world conditions is often limited by severe data imbalance, large variability between drivers, and the lack of physically interpretable vehicle dynamics representations.
• Score: 7.82687682320363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aggressive driving is a major cause of traffic accidents and poses a serious threat to road safety. Although deep learning methods have shown promising results in detecting risky driving behaviours from vehicle sensor data, their performance in real-world conditions is often limited by severe data imbalance, large variability between drivers, and the lack of physically interpretable vehicle dynamics representations. In this paper, we propose an enhanced deep learning framework for aggressive driving detection using multivariate vehicle dynamics signals. Instead of relying solely on raw measurements, the proposed approach constructs engineered dynamic features that capture steering, acceleration, and braking behaviour. To address the extreme rarity of aggressive events in naturalistic driving data, we introduce a stable training strategy that combines controlled SMOTE-based oversampling with a class-weighted loss formulation, and evaluates focal loss variants for imbalance handling. Furthermore, a safety-oriented decision strategy based on class-specific threshold calibration is adopted to better reflect the asymmetric risks of missed detections and false alarms in real-world applications. The proposed framework is evaluated on a newly collected naturalistic driving dataset. Extensive experiments show that the proposed method consistently outperforms standard deep learning baselines with significant improvements in minority-class recall and safety-critical F-score metrics while maintaining practical computational efficiency. Code: \url {https://github.com/halhamdan/CBANet}

Related papers

• From Attacks to Curricula: Learnability-Guided Adversarial Training for Safe Autonomous Driving [56.30087557121323]
  AlignADV is a learnability-guided closed-loop adversarial training framework.<n>It converts adversarial scenarios into resolvable and capability-aligned curricula.<n> Experiments show it reduces training steps by up to 40.6 percent.
  arXiv  Detail & Related papers  (2026-06-12T02:13:55Z)
• Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing [73.88859384645264]
  Out-Of-Distribution (OOD) generalization to various racetracks utilizes Machine Learning (ML) to encode the mathematical relation between sensor data and vehicle actuation for end-to-end control.<n>This paper presents a DRL method that parameterizes nonlinear vehicle dynamics from the spectral distribution of depth measurements with a non-geometric, physics-informed reward, to infer vehicle time-optimal and overtaking racing controls with an Artificial Neural Network (ANN)<n>The policy outperforms human demonstrations by 12% in OOD tracks on proportionally scaled hardware, by maximizing the friction circle with tire dynamics that resemble an empirical Pacejka tire model.
  arXiv  Detail & Related papers  (2026-04-10T17:12:07Z)
• Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation [10.706638374537306]
  We propose a behavior guided method for generating high risk lane change scenarios.<n>The proposed method can effectively learn high risk trajectory behaviors from limited data.
  arXiv  Detail & Related papers  (2026-03-08T12:46:02Z)
• ROAR: Robust Accident Recognition and Anticipation for Autonomous Driving [17.936492070548]
  Existing methods often assume ideal conditions, overlooking challenges such as sensor failures, environmental disturbances, and data imperfections.<n>This study introduces ROAR, a novel approach for accident detection and prediction.<n> ROAR combines Discrete Wavelet Transform (DWT), a self adaptive object aware module, and dynamic focal loss to tackle these challenges.
  arXiv  Detail & Related papers  (2025-11-09T04:55:37Z)
• How Much Is Too Much? Adaptive, Context-Aware Risk Detection in Naturalistic Driving [0.6299766708197883]
  We propose a unified, context-aware framework that adapts labels and models over time and across drivers.<n>The framework is tested using two safety indicators, speed-weighted headway and harsh driving events, and three models: Random Forest, XGBoost, and Deep Neural Network (DNN)<n>Overall, the framework shows promise for adaptive, context-aware risk detection that can enhance real-time safety feedback and support driver-focused interventions in intelligent transportation systems.
  arXiv  Detail & Related papers  (2025-07-26T16:24:25Z)
• Learning collision risk proactively from naturalistic driving data at scale [3.1457219084519004]
  This study introduces the Generalised Surrogate Safety Measure (GSSM)<n>GSSM learns collision risk from naturalistic driving without the need for crash or risk labels.<n> Diverse data from naturalistic driving, including motion kinematics, weather, lighting, etc., are used to train multiple GSSMs.<n>A basic GSSM using only instantaneous motion kinematics achieves an area under the precision-recall curve of 0.9 and secures a median time advance of 2.6 seconds to prevent potential collisions.
  arXiv  Detail & Related papers  (2025-05-19T07:22:32Z)
• Towards Reliable and Interpretable Traffic Crash Pattern Prediction and Safety Interventions Using Customized Large Language Models [14.53510262691888]
  TrafficSafe is a framework that adapts to reframe crash prediction and feature attribution as text-level reasoning.<n>Alcohol-impaired driving is the leading factor in severe crashes.<n>TrafficSafe highlights pivotal features during model training guiding strategic crash data collection improvements.
  arXiv  Detail & Related papers  (2025-05-18T21:02:30Z)
• Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models [39.139025989575686]
  We introduce an online, retrieval-augmented large language model (LLM) framework for generating safety-critical driving scenarios.<n>Our model reduces the mean minimum time-to-collision from 1.62 to 1.08 s and incurs a 75% collision rate, substantially outperforming baselines.
  arXiv  Detail & Related papers  (2025-05-02T03:22:00Z)
• Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
  Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
  arXiv  Detail & Related papers  (2024-11-04T09:21:00Z)
• RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes [57.319845580050924]
  We propose a reinforcement learning framework that combines risk-sensitive control with an adaptive action space curriculum. We show that our algorithm is capable of learning high-speed policies for a real-world off-road driving task.
  arXiv  Detail & Related papers  (2024-05-07T23:32:36Z)
• RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [46.909086734963665]
  This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior.<n>Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving.<n> RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
  arXiv  Detail & Related papers  (2023-12-12T06:21:30Z)
• CAT: Closed-loop Adversarial Training for Safe End-to-End Driving [54.60865656161679]
  Adversarial Training (CAT) is a framework for safe end-to-end driving in autonomous vehicles. Cat aims to continuously improve the safety of driving agents by training the agent on safety-critical scenarios. Cat can effectively generate adversarial scenarios countering the agent being trained.
  arXiv  Detail & Related papers  (2023-10-19T02:49:31Z)
• 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)

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.