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Accurate recognition of driver distraction is significant for the design of human-machine cooperation driving systems. Existing studies mainly focus on classifying varied distracted driving behaviors, which depend heavily on the scale and quality of datasets and only detect the discrete distraction categories. Therefore, most data-driven approaches have limited capability of recognizing unseen driving activities and cannot provide a reasonable solution for downstream applications. To address these challenges, this paper develops a vision Transformer-enabled weakly supervised contrastive (W-SupCon) learning framework, in which distracted behaviors are quantified by calculating their distances from the normal driving representation set. The Gaussian mixed model (GMM) is employed for the representation clustering, which centralizes the distribution of the normal driving representation set to better identify distracted behaviors. A novel driver behavior dataset and the other three ones are employed for the evaluation, experimental results demonstrate that our proposed approach has more accurate and robust performance than existing methods in the recognition of unknown driver activities. Furthermore, the rationality of distraction levels for different driving behaviors is evaluated through driver skeleton poses. The constructed dataset and demo videos are available at https://yanghh.io/Driver-Distraction-Quantification.

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Since emotions generally affect driver’s behavior, judgment, and reaction time, accurately identifying driver’s emotions is of great significance to improve the safety and comfort of intelligent driving system. However, the gender, skin color, age, and appearance of different drivers often have big differences, which will greatly interfere with the emotional recognition process. Besides, light intensity inside the vehicle varies with different time, weather, and location, which will also pose a challenge to driver emotion recognition. In this paper, a robust driver emotion recognition method based on feature separation is proposed to overcome the interference of individual differences and illumination changes. In order to realize the separation of expression-related features and irrelevant features, we design a high-purity feature separation (HPFS) framework based on partial feature exchange and the constraints of multiple loss functions. To verify that the proposed method can overcome the interference of illumination changes, we specifically create a multiple light intensities driver emotion recognition (MLI-DER) dataset and conduct a great deal of experiments on the dataset. In addition, to further demonstrate that our method can largely alleviate the interference of individual difference, some cross-subject emotion recognition experiments are conducted on two famous facial expression recognition datasets FACES and Oulu-CASIA and the experimental results are compared with that of some state-of-the-art methods.

Reinforcement learning has demonstrated its potential in a series of challenging domains. However, many real-world decision making tasks involve unpredictable environmental changes or unavoidable perception errors that are often enough to mislead an agent into making suboptimal decisions and even cause catastrophic failures. In light of these potential risks, reinforcement learning with application in safety-critical autonomous driving domain remains tricky without ensuring robustness against environmental uncertainties (e.g., road adhesion changes or measurement noises). Therefore, this paper proposes a novel constrained adversarial reinforcement learning approach for robust decision making of autonomous vehicles at highway on-ramps. Environmental disturbance is modelled as an adversarial agent that can learn an optimal adversarial policy to thwart the autonomous driving agent. Meanwhile, observation perturbation is approximated to maximize the variation of the perturbed policy through a white-box adversarial attack technique. Furthermore, a constrained adversarial actor-critic algorithm is presented to optimize an on-ramp merging policy while keeping the variations of the attacked driving policy and action-value function within bounds. Finally, the proposed robust highway on-ramp merging decision making method of autonomous vehicles is evaluated in three stochastic mixed traffic flows with different densities, and its effectiveness is demonstrated in comparison with the competitive baselines.