Investigating the trip configured causal effect of distracted driving on aggressive driving behavior for e-hailing taxi drivers

Risky driving behavior of taxi drivers typically evaluated for full operation or sometimes sorted into occupied and empty running trips.In this paper,we simultaneously analyze aggressive driving and distracted driving of taxi drivers under three different trip categories.Trip origin is considered a transition from without ride-order to with ride-order travelling or from with ride-order to occupied travelling,and a destination as a transition from occupied to without ride-order travelling and vice versa.Distracted driving is characterized by driver interference,driver mobile use and some entertainment aspects,while specific harmful and risky actions are considered for aggressive driving.High-resolution and real-time kinematic parameters of taxis were recorded by the in-vehicle recorder VBOX for overall 562 trips.The distracted driving parameters and aggressive driving actions were monitored through python data collector web application that was specially programmed for this particular research.Besides dual dash cam(i.e.,front and inside car camera),drivers’ whole day driving history from their Chinese ride-hailing Di Di smart application was used to differentiate occupied trips,unoccupied trips with ride-order and unoccupied trips without ride-order.Structural equation modeling(SEM) is practiced in this paper to understand the influence of distracted driving indicators on aggressive driving behaviors.The multi-group model analysis of SEM indicated that handling distracted risky driving could control aggressive driving behavior up to 96% and 98% inunoccupied without ride-order trips and unoccupied trips with ride-order respectively.The model has also identified the sensitive risky driving indicators for each group separately.

Probabilistic Lane-Change Decision-Making and Planning for Autonomous Heavy Vehicles

To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index(AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.