With the development of automated driving systems and V2I (vehicle-to-infrastructure) communications, soft-safety driver alerts can be implemented to supplement imminent driver alerts. This type of alert improves drivers’ situational awareness of emerging risks over a more extended period with more detailed incident information and longer response time. However, compared to the large number of studies focusing on imminent risks, there are insufficient studies on the evaluation and effectiveness of soft-safety alerts. This study proposed an innovative metric to assess the comfort and safety of soft-safety driver alerts by constructing an ideal speed profile and calculating the deviation between the actual and ideal profile. We select the highway end-of-queue event as the experiment scenario, which is a leading cause of fatal highway crashes. Human subjects’ experiments are conducted in the driving simulator to validate the proposed metrics. The results have proved that the proposed metrics have good potential to assess driving comfort objectively. We also found that soft-safety alerts tend to improve driving comfort. However, there is insufficient evidence to conclude statistically about the prototype soft-safety alerts implemented in the experiments.

Pedestrian trajectory prediction is a critical component of autonomous driving in urban environments, allowing vehicles to anticipate pedestrian movements and facilitate safer interactions. While egocentric-view-based algorithms can reduce the sensing and computation burdens of 3D scene reconstruction, accurately predicting pedestrian trajectories and interpreting their intentions from this perspective requires a better understanding of the coupled vehicle (camera) and pedestrian motions, which has not been adequately addressed by existing models. In this paper, we present a novel egocentric pedestrian trajectory prediction approach that uses a two-tower structure and multi-modal inputs. One tower, the vehicle module, receives only the initial pedestrian position and ego-vehicle actions and speed, while the other, the pedestrian module, receives additional prior pedestrian trajectory and visual features. Our proposed action-aware loss function allows the two-tower model to decompose pedestrian trajectory predictions into two parts, caused by ego-vehicle movement and pedestrian movement, respectively, even when only trained on combined ego-view motions. This decomposition increases model flexibility and provides a better estimation of pedestrian actions and intentions, enhancing overall performance. Experiments on three publicly available benchmark datasets show that our proposed model outperforms all existing algorithms in ego-view pedestrian trajectory prediction accuracy.

With rapid development in hardware (sensors and processors) and AI algorithms, automated driving techniques have entered the public’s daily life and achieved great success in supporting human driving performance. However, due to the high contextual variations and temporal dynamics in pedestrian behaviors, the interaction between autonomous-driving cars and pedestrians remains challenging, impeding the development of fully autonomous driving systems. This paper focuses on predicting pedestrian intention with a novel transformer-based evidential prediction (TrEP) algorithm. We develop a transformer module towards the temporal correlations among the input features within pedestrian video sequences and a deep evidential learning model to capture the AI uncertainty under scene complexities. Experimental results on three popular pedestrian intent benchmarks have verified the effectiveness of our proposed model over the state-of-the-art. The algorithm performance can be further boosted by controlling the uncertainty level. We systematically compare human disagreements with AI uncertainty to further evaluate AI performance in confusing scenes. The code is released at https://github.com/zzmonlyyou/TrEP.git.