A Comparative Study of Ego-centric and Cooperative Perception for Lane Change Prediction in Highway Driving Scenarios

Abstract

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-ofthe-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.

Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-Based Convolutional Neural Networks

“…The vehicles' tracking history, as well as the position of road markings, are assumed to be shared with the EV for the problem of LC prediction. Note that the vehicles' tracking data can also be obtained from egocentric perception sensors (instead of infrastructure sensors) with less coverage and accuracy due to their limited range and occlusion.We refer readers to [40] for a comparative study of different perception approaches for lane change prediction in highways.…”

Discussion of “some statistical challenges in automated driving systems”

“…The perception of other road users' intentions and the environment can be achieved through either an ego-centric method, which relies on ADS onboard sensors, or a cooperative method, which involves communication with other road users and infrastructure via connected vehicle technology. 4 While connected vehicle technology can provide more accurate information; it is important to acknowledge that the majority of vehicles on today's road are not connected, and it will likely take decades to replace them all with connected vehicles. Therefore, the ego-centric approach currently dominates, and predicting the behavior of other road users has become a prominent research topic.…”

“…The utilization of game theory and adversarial risk analysis (ARA) methodology to model ADS behavior in mixed traffic with human drivers is a brilliant approach for this paper. The perception of other road users' intentions and the environment can be achieved through either an ego‐centric method, which relies on ADS onboard sensors, or a cooperative method, which involves communication with other road users and infrastructure via connected vehicle technology 4 . While connected vehicle technology can provide more accurate information; it is important to acknowledge that the majority of vehicles on today's road are not connected, and it will likely take decades to replace them all with connected vehicles.…”