This paper presents an analysis of rearward gap acceptance characteristics of drivers of large trucks in highway lane change scenarios. The range between the vehicles was inferred from camera images using the estimated lane width obtained from the lane tracking camera as the reference. Six-hundred lane change events were acquired from a large-scale naturalistic driving data set. The kinematic variables from the image-based gap analysis were filtered by the weighted linear least squares in order to extrapolate them at the lane change time. In addition, the time-to-collision and required deceleration were computed, and potential safety threshold values are provided. The resulting range and range rate distributions showed directional discrepancies, i.e., in left lane changes, large trucks are often slower than other vehicles in the target lane, whereas they are usually faster in right lane changes. Video observations have confirmed that major motivations for changing lanes are different depending on the direction of move, i.e., moving to the left (faster) lane occurs due to a slower vehicle ahead or a merging vehicle on the right-hand side, whereas right lane changes are frequently made to return to the original lane after passing.

A Time-To-Rollover (TTR) metric is proposed as the basis to assess rollover threat for an articulated vehicle. Ideally, a TTR metric will accurately “count-down” toward rollover regardless of vehicle speed and steering patterns, so that the level of rollover threat is accurately indicated. To implement TTR in real-time, there are two conflicting requirements. On the one hand, a faster-than-real-time model is needed. On the other hand, the TTR predicted by this model needs to be accurate enough under all driving scenarios. An innovative approach is proposed in this paper to solve this dilemma and the whole process is illustrated in a design example. First, a simple yet reasonably accurate yaw/roll model is identified. A Neural Network (NN) is then developed to mitigate the accuracy problem of this simplified real-time model. The NN takes the TTR generated by the simplified model, vehicle roll angle and change of roll angle to generate an enhanced NN-TTR index. The NN was trained and verified under a variety of driving patterns. It was found that an accurate TTR is achievable across all the driving scenarios we tested.

It is important to rigorously and comprehensively evaluate the safety of Automated Vehicles (AVs) before their production and deployment. A popular AV evaluation approach is Naturalistic-Field Operational Test (N-FOT) which tests prototype vehicles directly on public roads. Due to the low exposure to safety-critical scenarios, N-FOTs is time-consuming and expensive to conduct. Computer simulations can be used as an alternative to N-FOTs, especially in terms of generating motions of the surrounding traffic. In this paper, we propose an accelerated evaluation approach for AVs. Human-controlled vehicles (HVs) were modeled as disturbance to AVs based on data extracted from the Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behavior, which amplifies riskier testing scenarios while reserves its statistical information so that the safety benefits of AV in non-accelerated cases can be accurately estimated. An AV model based on a production vehicle was tested. Results show that the proposed method can accelerate the evaluation process by at least 100 times.

Young’s double-slit experiment had been performed over 200 years ago. Two- dimension cross-double-slit has been proposed recently. We report observations of two-dimension-cross-interference patterns created by two-dimension cross-double-slit apparatuses.