MReza Alipour Sormoli

MReza Alipour Sormoli, Amir Samadi, Sajjad Mozaffari et al.

Anticipating the motion of other road users is crucial for automated driving systems (ADS), as it enables safe and informed downstream decision-making and motion planning. Unfortunately, contemporary learning-based approaches for motion prediction exhibit significant performance degradation as the prediction horizon increases or the observation window decreases. This paper proposes a novel technique for trajectory prediction that combines a data-driven learning-based method with a velocity vector field (VVF) generated from a nature-inspired concept, i.e., fluid flow dynamics. In this work, the vector field is incorporated as an additional input to a convolutional-recurrent deep neural network to help predict the most likely future trajectories given a sequence of bird's eye view scene representations. The performance of the proposed model is compared with state-of-the-art methods on the HighD dataset demonstrating that the VVF inclusion improves the prediction accuracy for both short and long-term (5~sec) time horizons. It is also shown that the accuracy remains consistent with decreasing observation windows which alleviates the requirement of a long history of past observations for accurate trajectory prediction. Source codes are available at: https://github.com/Amir-Samadi/VVF-TP.

Sajjad Mozaffari, Mreza Alipour Sormoli, Konstantinos Koufos et al.

Accurate trajectory prediction of nearby vehicles is crucial for the safe motion planning of automated vehicles in dynamic driving scenarios such as highway merging. Existing methods cannot initiate prediction for a vehicle unless observed for a fixed duration of two or more seconds. This prevents a fast reaction by the ego vehicle to vehicles that enter its perception range, thus creating safety concerns. Therefore, this paper proposes a novel transformer-based trajectory prediction approach, specifically trained to handle any observation length larger than one frame. We perform a comprehensive evaluation of the proposed method using two large-scale highway trajectory datasets, namely the highD and exiD. In addition, we study the impact of the proposed prediction approach on motion planning and control tasks using extensive merging scenarios from the exiD dataset. To the best of our knowledge, this marks the first instance where such a large-scale highway merging dataset has been employed for this purpose. The results demonstrate that the prediction model achieves state-of-the-art performance on highD dataset and maintains lower prediction error w.r.t. the constant velocity across all observation lengths in exiD. Moreover, it significantly enhances safety, comfort, and efficiency in dense traffic scenarios, as compared to the constant velocity model.

Idil Ozdamar, M. Reza Alipour, Benoit P. Delhaye et al.

Rendering tactile effects on a touch screen via electrovibration has many potential applications. However, our knowledge on tactile perception of change in friction and the underlying contact mechanics are both very limited. In this study, we investigate the tactile perception and the contact mechanics for a step change in friction under electrovibration during a relative sliding between finger and the surface of a capacitive touchscreen. First, we conduct magnitude estimation experiments to investigate the role of normal force and sliding velocity on the perceived tactile intensity for a step increase and decrease in friction, called as rising friction (RF) and falling friction (FF). To investigate the contact mechanics involved in RF and FF, we then measure the frictional force, the apparent contact area, and the strains acting on the fingerpad during sliding at a constant velocity under three different normal loads using a custom-made experimental set-up. The results show that the participants perceived RF stronger than FF, and both the normal force and sliding velocity significantly influenced their perception. These results are supported by our mechanical measurements; the relative change in friction, the apparent contact area, and the strain in the sliding direction were all higher for RF than those for FF, especially for low normal forces. Taken together, our results suggest that different contact mechanics take place during RF and FF due to the viscoelastic behavior of fingerpad skin, and those differences influence our tactile perception of a step change in friction.