相关论文: PSI: A Benchmark for Human Interpretation and Resp…

In light of growing attention of intelligent vehicle systems, we propose developing a driver model that uses a hybrid system formulation to capture the intent of the driver. This model hopes to capture human driving behavior in a way that…

In the driving scene, the road agents usually conduct frequent interactions and intention understanding of the surroundings. Ego-agent (each road agent itself) predicts what behavior will be engaged by other road users all the time and…

This study presents the outcomes of empirical investigations pertaining to human-vehicle interactions involving an autonomous vehicle equipped with both internal and external Human Machine Interfaces (HMIs) within a crosswalk scenario. The…

Autonomous vehicles often make complex decisions via machine learning-based predictive models applied to collected sensor data. While this combination of methods provides a foundation for real-time actions, self-driving behavior primarily…

The advancement of socially-aware autonomous vehicles hinges on precise modeling of human behavior. Within this broad paradigm, the specific challenge lies in accurately predicting pedestrian's trajectory and intention. Traditional…

Most existing traffic sign-related works are dedicated to detecting and recognizing part of traffic signs individually, which fails to analyze the global semantic logic among signs and may convey inaccurate traffic instruction. Following…

One of the most crucial yet challenging tasks for autonomous vehicles in urban environments is predicting the future behaviour of nearby pedestrians, especially at points of crossing. Predicting behaviour depends on many social and…

Understanding the behaviors and intentions of humans are one of the main challenges autonomous ground vehicles still faced with. More specifically, when it comes to complex environments such as urban traffic scenes, inferring the intentions…

Predicting pedestrian behavior is one of the main challenges for intelligent driving systems. In this paper, we present a new paradigm for evaluating egocentric pedestrian trajectory prediction algorithms. Based on various contextual…

With the rapid advancements in autonomous driving, accurately predicting pedestrian behavior has become essential for ensuring safety in complex and unpredictable traffic conditions. The growing interest in this challenge highlights the…

Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario.…

The major advances in intelligent transportation systems are pushing societal services toward autonomy where road management is to be more agile in order to cope with changes and continue to yield optimal performance. However, the…

Assessing drivers' interaction capabilities is crucial for understanding human driving behavior and enhancing the interactive abilities of autonomous vehicles. In scenarios involving strong interaction, existing metrics focused on…

Effective driving style analysis is critical to developing human-centered intelligent driving systems that consider drivers' preferences. However, the approaches and conclusions of most related studies are diverse and inconsistent because…

Action and intention recognition of pedestrians in urban settings are challenging problems for Advanced Driver Assistance Systems as well as future autonomous vehicles to maintain smooth and safe traffic. This work investigates a number of…

Minimizing traffic accidents between vehicles and pedestrians is one of the primary research goals in intelligent transportation systems. To achieve the goal, pedestrian orientation recognition and prediction of pedestrian's crossing or…

Autonomous vehicles (AVs) are becoming an indispensable part of future transportation. However, safety challenges and lack of reliability limit their real-world deployment. Towards boosting the appearance of AVs on the roads, the…

Detecting and predicting the behavior of pedestrians is extremely crucial for self-driving vehicles to plan and interact with them safely. Although there have been several research works in this area, it is important to have fast and memory…

To maximize safety and driving comfort, autonomous driving systems can benefit from implementing foresighted action choices that take different potential scenario developments into account. While artificial scene prediction methods are…

Autonomous vehicles must negotiate with pedestrians in ways that are both safe and socially compliant. We present an interaction-aware model predictive decision-making (IAMPDM) framework that integrates a gap-acceptance-inspired intention…