CPSOR-GCN: A Vehicle Trajectory Prediction Method Powered by Emotion and
Cognitive Theory
- URL: http://arxiv.org/abs/2311.08086v1
- Date: Tue, 14 Nov 2023 11:13:00 GMT
- Title: CPSOR-GCN: A Vehicle Trajectory Prediction Method Powered by Emotion and
Cognitive Theory
- Authors: L. Tang, Y. Li, J. Yuan, A. Fu, J. Sun
- Abstract summary: This paper proposes a new trajectory prediction model: CPSOR-GCN.
At the physical level, the interaction features between vehicles are extracted by the physical GCN module.
At the cognitive level, SOR cognitive theory is used as prior knowledge to build a Dynamic Bayesian Network (DBN) structure.
- Score: 0.07499722271664144
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Active safety systems on vehicles often face problems with false alarms. Most
active safety systems predict the driver's trajectory with the assumption that
the driver is always in a normal emotion, and then infer risks. However, the
driver's trajectory uncertainty increases under abnormal emotions. This paper
proposes a new trajectory prediction model: CPSOR-GCN, which predicts vehicle
trajectories under abnormal emotions. At the physical level, the interaction
features between vehicles are extracted by the physical GCN module. At the
cognitive level, SOR cognitive theory is used as prior knowledge to build a
Dynamic Bayesian Network (DBN) structure. The conditional probability and state
transition probability of nodes from the calibrated SOR-DBN quantify the causal
relationship between cognitive factors, which is embedded into the cognitive
GCN module to extract the characteristics of the influence mechanism of
emotions on driving behavior. The CARLA-SUMO joint driving simulation platform
was built to develop dangerous pre-crash scenarios. Methods of recreating
traffic scenes were used to naturally induce abnormal emotions. The experiment
collected data from 26 participants to verify the proposed model. Compared with
the model that only considers physical motion features, the prediction accuracy
of the proposed model is increased by 68.70%. Furthermore,considering the
SOR-DBN reduces the prediction error of the trajectory by 15.93%. Compared with
other advanced trajectory prediction models, the results of CPSOR-GCN also have
lower errors. This model can be integrated into active safety systems to better
adapt to the driver's emotions, which could effectively reduce false alarms.
Related papers
- Masked EEG Modeling for Driving Intention Prediction [27.606175591082756]
This paper pioneers a novel research direction in BCI-assisted driving, studying the neural patterns related to driving intentions.
We propose a novel Masked EEG Modeling framework for predicting human driving intentions, including the intention for left turning, right turning, and straight proceeding.
Our model attains an accuracy of 85.19% when predicting driving intentions for drowsy subjects, which shows its promising potential for mitigating traffic accidents related to drowsy driving.
arXiv Detail & Related papers (2024-08-08T03:49:05Z) - MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction [24.3907895281179]
This paper presents a novel deep learning model designed for counterfactual post-crash traffic prediction.
The proposed model is treatment-aware, with a specific focus on comprehending and predicting traffic speed under hypothetical crash intervention strategies.
The model is validated using both synthetic and real-world data, demonstrating that MSCT outperforms state-of-the-art models in multi-step-ahead prediction performance.
arXiv Detail & Related papers (2024-07-19T06:42:41Z) - Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
Motion planners (MPs) are crucial for safe navigation in complex urban environments.
nuPlan, a recently released MP benchmark, addresses this limitation by augmenting real-world driving logs with closed-loop simulation logic.
We present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
arXiv Detail & Related papers (2024-06-15T18:53:45Z) - Hacking Predictors Means Hacking Cars: Using Sensitivity Analysis to Identify Trajectory Prediction Vulnerabilities for Autonomous Driving Security [1.949927790632678]
In this paper, we conduct a sensitivity analysis on two trajectory prediction models, Trajectron++ and AgentFormer.
The analysis reveals that between all inputs, almost all of the perturbation sensitivities for both models lie only within the most recent position and velocity states.
We additionally demonstrate that, despite dominant sensitivity on state history perturbations, an undetectable image map perturbation can induce large prediction error increases in both models.
arXiv Detail & Related papers (2024-01-18T18:47:29Z) - RACER: Rational Artificial Intelligence Car-following-model Enhanced by
Reality [51.244807332133696]
This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior.
Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving.
RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
arXiv Detail & Related papers (2023-12-12T06:21:30Z) - Cognitive Accident Prediction in Driving Scenes: A Multimodality
Benchmark [77.54411007883962]
We propose a Cognitive Accident Prediction (CAP) method that explicitly leverages human-inspired cognition of text description on the visual observation and the driver attention to facilitate model training.
CAP is formulated by an attentive text-to-vision shift fusion module, an attentive scene context transfer module, and the driver attention guided accident prediction module.
We construct a new large-scale benchmark consisting of 11,727 in-the-wild accident videos with over 2.19 million frames.
arXiv Detail & Related papers (2022-12-19T11:43:02Z) - AdvDO: Realistic Adversarial Attacks for Trajectory Prediction [87.96767885419423]
Trajectory prediction is essential for autonomous vehicles to plan correct and safe driving behaviors.
We devise an optimization-based adversarial attack framework to generate realistic adversarial trajectories.
Our attack can lead an AV to drive off road or collide into other vehicles in simulation.
arXiv Detail & Related papers (2022-09-19T03:34:59Z) - Control-Aware Prediction Objectives for Autonomous Driving [78.19515972466063]
We present control-aware prediction objectives (CAPOs) to evaluate the downstream effect of predictions on control without requiring the planner be differentiable.
We propose two types of importance weights that weight the predictive likelihood: one using an attention model between agents, and another based on control variation when exchanging predicted trajectories for ground truth trajectories.
arXiv Detail & Related papers (2022-04-28T07:37:21Z) - 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) - A model for traffic incident prediction using emergency braking data [77.34726150561087]
We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents.
We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
arXiv Detail & Related papers (2021-02-12T18:17:12Z)
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.