Automated Vehicles (AVs) must deal with uncivil human behaviors and may even encourage new ones. To provide an in-depth understanding of such behaviors, we studied interactions between AVs (shuttles, personal cars, delivery robots) and other road users on highways, urban roads, and pedestrian areas based on extensive data from 6 real-world tests in France. These interactions were categorized into prototypical cases, which were analyzed using a descriptive model we developed to capture their situational, motivational, and behavioral factors. The results show that uncivil behavioral patterns involve mixed emotional and trajectory management, focusing on displacement, space sharing, or personal interaction. Uncivil behavior is thus defined by its disrespectful outcomes on traffic fluidity, disputed right of way, and personal offense (e.g., space intrusion, aggressivity, griefing of AVs). Improved infrastructure, better information for other road users, and enhanced AV behavior could mitigate uncivil behaviors and facilitate AV acceptance.

This paper describes the design of a new haptic shared steering control framework for automated driving systems. In this framework, the shared control problem is formulated as a constrained optimization problem which is solved online by a model predictive controller. Without driver's intervention, the system assumes automatic lane-keeping control. When the driver takes over control, by adapting the weight on the stage cost and implementing dynamic constraints, the framework ensures seamless control transfer from the system to the driver while conveying potential hazards through haptic feedback. Simulation results are presented to demonstrate the ability of this framework to handle control allocation and hazard warning.

This paper describes an adaptive vehicle longitudinal trajectory prediction method for automated highway driving applications. A major strength of this method is that it can cope with highly dynamic situations in which the constant acceleration (CA) assumption cannot guarantee long term prediction accuracy. In this method, a quintic polynomial is used to model the longitudinal dynamics of a vehicle that is maneuvering. The decision to switch to it from the CA model is formulated as a maneuver detection problem. A maneuver is detected through monitoring measurement innovations of a Kalman filter that tracks target longitudinal states. The longitudinal jerk, as a dynamic characteristic of a maneuver is also estimated from measurement innovations. Finally the estimated jerk and context information are incorporated into the quintic polynomial model. The overall approach was tested on recorded human driving data from a simulator in a dynamic highway merging scenario. The results show the proposed method has higher prediction accuracy than the CA based method in such a dynamic scenario.

This paper describes the design of a shared control framework for vehicle longitudinal control applied in Automated Driving (AD) systems. Due to the complexity of automation design, highway merging scenario is firstly selected as the use case for system's functional design. Besides the distance control functionality, the framework is capable of managing merging situation thanks to the proposed situation assessment function. In order to provide the driver a mean to override the operating automated system, shared control logics are designed for accelerator and brake pedal respectively. In case of driver's override on accelerator, hap tic feedback is rendered aiming to communicate the system's activity and situation's criticality. Preliminary simulation results are presented to demonstrate the design concepts of the framework.