More Like this

1. Validation and Analysis of Driving Safety Assessment Metrics in Real-world Car-Following Scenarios with Aerial Videos

   https://doi.org/10.4271/2024-01-2020  

   Lu, Duo; Haines, Sam; Jammula, Varun Chandra; Rath, Prabin Kumar; Yu, Hongbin; Yang, Yezhou; Wishart, Jeffrey (April 2024, SAE)

   Data-driven driving safety assessment is crucial in understanding the insights of traffic accidents caused by dangerous driving behaviors. Meanwhile, quantifying driving safety through well-defined metrics in real-world naturalistic driving data is also an important step for the operational safety assessment of automated vehicles (AV). However, the lack of flexible data acquisition methods and fine-grained datasets has hindered progress in this critical area. In response to this challenge, we propose a novel dataset for driving safety metrics analysis specifically tailored to car-following situations. Leveraging state-of-the-art Artificial Intelligence (AI) technology, we employ drones to capture high-resolution video data at 12 traffic scenes in the Phoenix metropolitan area. After that, we developed advanced computer vision algorithms and semantically annotated maps to extract precise vehicle trajectories and leader-follower relations among vehicles. These components, in conjunction with a set of defined metrics based on our prior work on Operational Safety Assessment (OSA) by the Institute of Automated Mobility (IAM), allow us to conduct a detailed analysis of driving safety. Our results reveal the distribution of these metrics under various real-world car-following scenarios and characterize the impact of different parameters and thresholds in the metrics. By enabling a data-driven approach to address driving safety in car-following scenarios, our work can empower traffic operators and policymakers to make informed decisions and contribute to a safer, more efficient future for road transportation systems. 

   more » « less
2. Gait Differences in the Real World and Virtual Reality: The Effect of Prior Virtual Reality Experience

   https://doi.org/10.1109/ISMAR55827.2022.00080  

   Nasiri, Moloud; Anaraky, Reza Ghaiumy; Babu, Sabarish V.; Robb, Andrew Robb (October 2022, IEEE International Conference on Virtual Reality and 3D User Interfaces (IEEE VR) 2022)

   Walking through immersive virtual environments is one of the important parts of Virtual Reality (VR) applications. Prior research has established that users’ gait in virtual and real environments differs; however, little research has evaluated how users’ gait differs as users gain more experience with VR. We conducted experiments measuring novice and experienced subjects’ gait parameters in VR and real environments. Results showed that subjects’ performance in VR and Real World was more similar in the last trials than in the first trials; their walking dissimilarity in the start trials diminished by walking more trials. We found trial as a significant variable affecting the walking speed, step length, and trunk angle for both groups of users. While the main effect of expertise was not observed, an interaction effect between expertise and the trial number was shown. Trunk angle increased over time for novices but decreased for experts. 

   more » « less
3. Evaluating the dissociation between drivers’ self-perceived and objective need for vehicle assistance during obstacle avoidance tasks

   https://doi.org/10.1177/21695067231192620  

   Luster, Maya S; Pitts, Brandon J (September 2023, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Driver-assistance systems are becoming more commonplace; however, the realized safety benefits of these technologies depend on whether a person accepts and adopts automated driving aids. One challenge to adoption could be a preference-performance dissociation (PPD), which is a mismatch between a self-perceived desire and an objective need for assistance. Research has reported PPD in driving but has not extensively leveraged driving performance data to confirm its existence. Thus, the goal of this study was to compare drivers’ self-reported need for vehicle assistance to their objective driving performance. Twenty-one participants drove on a simulated road and traversed challenging, real-world roadway obstacles. Afterwards, they were asked about their preference for automated vehicle assistance (e.g., steering and braking) during their drive. Overall, some participants exhibited PPD that included both over- and underestimating their need for a particular type of automated assistance. Findings can be used to develop shared control and adaptive automation strategies tailored to particular users and contexts across various safety-critical environments. 

   more » « less
4. Real-Time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control

   https://doi.org/10.1109/SMC53992.2023.10394260  

   Zhao, Zhouqiao; Liao, Xishun; Abdelraouf, Amr; Han, Kyungtae; Gupta, Rohit; Barth, Matthew J; Wu, Guoyuan (October 2023, IEEE)

   Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used. However, pre-defined ACC settings may not always align with driver's preferences and habits, leading to discomfort and potential safety issues. Personalized ACC (P-ACC) has been proposed to address this problem, but most existing research uses historical driving data to imitate behaviors that conform to driver preferences, neglecting real-time driver feedback. To bridge this gap, we propose a cloud-vehicle collaborative P-ACC framework that incorporates driver feedback adaptation in real time. The framework is divided into offline and online parts. The offline component records the driver's naturalistic car-following trajectory and uses inverse reinforcement learning (IRL) to train the model on the cloud. In the online component, driver feedback is used to update the driving gap preference in real time. The model is then retrained on the cloud with driver's takeover trajectories, achieving incremental learning to better match driver's preference. Human-in-the-loop (HuiL) simulation experiments demonstrate that our proposed method significantly reduces driver intervention in automatic control systems by up to 62.8%. By incorporating real-time driver feedback, our approach enhances the comfort and safety of P-ACC, providing a personalized and adaptable driving experience. 

   more » « less
5. WIP: Towards the Practicality of the Adversarial Attack on Object Tracking in Autonomous Driving

   Ma, Chen; Wang, Ningfei; Chen, Qi Alfred; Shen, Chao (January 2023, ISOC Symposium on Vehicle Security and Privacy (VehicleSec))

   Recently, adversarial examples against object detection have been widely studied. However, it is difficult for these attacks to have an impact on visual perception in autonomous driving because the complete visual pipeline of real-world autonomous driving systems includes not only object detection but also object tracking. In this paper, we present a novel tracker hijacking attack against the multi-target tracking algorithm employed by real-world autonomous driving systems, which controls the bounding box of object detection to spoof the multiple object tracking process. Our approach exploits the detection box generation process of the anchor-based object detection algorithm and designs new optimization methods to generate adversarial patches that can successfully perform tracker hijacking attacks, causing security risks. The evaluation results show that our approach has 85% attack success rate on two detection models employed by real-world autonomous driving systems. We discuss our potential next step for this work. 

   more » « less