Teleoperation provides the human operator with sophisticated perceptual and cognitive skills in an over-the-network control loop. It gives hope of addressing some challenges related to vehicular autonomy which is based on artificial intelligence by providing a fallback plan. Variable network time-delay in data transmission is the major problem in teleoperating a vehicle. On 4G network, variability of this delay is significant (70-150 ms ping). Due to this, both video streaming and driving commands encounter variable time-delay. This paper presents an approach to provide the human-operator with a forecasted video stream that replicates future perspective of vehicle’s field of view accounting for the delay present in the network. Regarding the image transformation, perspective projection technique is combined with correction given by Smith predictor in the control loop. This image transformation accounts current time-delay and tries to address both issues, time-delays as well as variability. For experiment sake, only frontward field of view is forecasted. Performance is evaluated by performing vehicle teleoperation with real vehicle on street edge-case maneuvers and later comparing the cross-track error with and without perspective projection. Results obtained show improvement in path following tasks.

Vehicle teleoperation has the ability to bridge the gap between completely automated driving and manual driving by remotely monitoring and operating autonomous vehicles when their automation fails. Among many challenges related to vehicle teleoperation, the considered ones in this work are variable time delay, saturation of actuators installed in vehicle, and environmental disturbance, which together limit the teleoperation performance. State-of-the-art predictive techniques estimate vehicle states to compensate for the delays, but the predictive states do not account for sudden disturbances that the vehicle observes, which makes the human-picked steer inadequate. This inadequacy of steer deteriorates the path-tracking performance of vehicle teleoperation. In the proposed successive reference-pose-tracking (SRPT) approach, instead of transmitting steering commands, the reference trajectory, in the form of successive reference poses, is transmitted to the vehicle. This paper introduces a method of generation of successive reference poses with a joystick steering wheel and compares the human-in-loop path-tracking performance of the Smith predictor and SRPT approach. Human-in-loop experiments (with 18 different drivers) are conducted using a simulation environment that consists of the integration of a real-time 14-DOF Simulink vehicle model and Unity game engine in the presence of bidirectional variable delays. Scenarios for performance comparison are low adhesion ground, strong lateral wind, tight corners, and sudden obstacle avoidance. Result shows significant improvement in reference tracking and in reducing human effort in all scenarios using the SRPT approach.

Vehicle teleoperation holds potential applications as a fallback solution for autonomous vehicles, remote delivery services, hazardous operations, etc. However, network delays and limited situational awareness can compromise teleoperation performance and increase the cognitive workload of human operators. To address these issues, we previously introduced the novel successive reference pose tracking (SRPT) approach, which transmits successive reference poses to the vehicle instead of steering commands. This paper compares the stability and performance of SRPT with Smith predictor-based approach for direct vehicle teleoperation in challenging scenarios. The Smith predictor approach is further categorized, one with Lookahead driver and second with Stanley driver. Simulations are conducted in a Simulink environment, considering variable network delays (250-350 ms) and different vehicle speeds (14-26 km/h), and include maneuvers such as tight corners, slalom, low-adhesion roads, and strong crosswinds. The results show that the SRPT approach significantly improves stability and reference tracking performance, with negligible effect of network delays on path tracking. Our findings demonstrate the effectiveness of SRPT in eliminating the detrimental effect of network delays in vehicle teleoperation.

People detection and counting is becoming of great relevance in many application fields which range from the video surveillance of shopping centres to the management of public services such as bus stops. Various computer vision applications exploit Deep Learning technology for people recognition which recently showed great achievements. A very important task along with object/people detection is their tracking which is of fundamental importance especially when counting has to be performed. In this research work, a specific application for people counting at a bus stop is proposed, taking advantage of NVIDIA DeepStream SDK 5.0 detection and tracking algorithms. The final number of people is obtained through a Kalman Filter which aims at avoiding occlusion issues which are typical of people detection-tracking applications. The experimental results show the effectiveness of this approach in three scenarios with different complexity, significantly reducing the counting error with respect to pure detection counting.

Remote driving, i.e., the capacity of controlling road vehicles at a distance, is an innovative transportation technology often associated with potential ethical benefits, especially when deployed to tackle urban traffic issues. However, prospected benefits could only be reaped if remote driving can be executed in a safe and responsible way. This paper builds on notions elaborated in the philosophical literature on technological mediation to offer a systematic examination of the extent to which current and emerging Human–Machine Interfaces contribute to hindering or supporting the exercise of responsibility behind the remote wheel. More specifically, the analysis discusses how video, audio, and haptic interfaces co-shape the remote driving experience and, at the same time, the operators’ capacity to drive responsibly. The multidisciplinary approach explored in this research offers a novel methodological framework to structure future empirical inquiries while identifying finely tuned multi-sensory HMIs and dedicated training as critical presuppositions to the remote drivers’ exercise of responsibility.

See-through displays allows user to view the scene behind the display. See-through displays are playing a crucial role in shaping augmented reality applications. However, the cost of the transparent displays are higher than the normal displays. In this paper, we disclose a low cost digitally transparent display using a depth camera, two color cameras and a regular display. The method described in this paper tracks the eyes of the user and finds the corresponding distance from the depth sensor and renders the rear camera preview of the tablet or mobile in such a way that the user perceives it as a transparent display. This method has the potential to change the augmented reality applications in tablets or mobiles from a video see-through experience to a glass see-through experience