I hope that I am preaching to the choir by stating this to the readers of IEEE Vehicular Technology Magazine, but to me, autonomous vehicles should be connected. Autonomous vehicles may be combining data from cameras, onboard sensors, and lidars, they may be intelligent, learning, and adapting to each situation; but, if they are not connected, then they are not very smart. Active safety functions have been taken about as far as they can go. To advance safety further, connectivity is needed. Communications can be seen as a different type of sensor, providing the nonline-of-sight information that radars cannot. During fog, where the camera cannot see, communications can relay information from the vehicle in front, and, similarly, it can relay information about the driver's intentions, which the lidar is unable to detect. Detecting a human in the dark by using machine learning is difficult, and the algorithms can be fooled. Incidents that seem straightforward to humans may mislead the automated vehicles and vice versa-getting drunk, sleepy, and distracted seems irrational to hazard classification algorithms. Uber and Tesla are self-driving but not connected and cooperating. Could the accidents have been prevented if the autonomous vehicles were connected?

The interest in wireless communication systems for industrial applications has grown significantly over the last years. More flexible, easier to install and maintain, wireless networks present a promising alternative to the currently used wired systems. However, reliability and timeliness requirements at present met by wired networks also need to be fulfilled by wireless solutions. Packet errors introduced when packets travel through wireless channels imply a significant challenge to fulfill these requirements. Relaying has been recognized to improve the reliability in industrial wireless networks without causing additional delay. Furthermore, the recent results have shown that relaying combined with packet aggregation significantly outperforms simple relaying. However, it is not always cost efficient to introduce additional relay nodes into an industrial network and hence, in this paper, we propose using a combination of relaying and packet aggregation at the source nodes. The results show that when relaying and aggregation are used at the source nodes, the transmission schedule plays a crucial role. A schedule adapting to the varying channel conditions improves performance substantially. By carefully choosing which packet to aggregate, even further improvements can be achieved.

The benefits of platooning, e.g., fuel efficiency, road throughput enhancement, driver offload, etc., have sparked an interest in a more connected, intelligent, and sustainable transportation ecosystem. However, efficient platooning is realized through wireless communications, characterized by transient connectivity, which is caused by occasional packet losses. Being a safety-critical system of systems, a platoon must be fail-operational even during transient connectivity. Moreover, a platoon should be capable of transitioning into a fail-safe state upon encountering a hazard. To this end, we propose a strategy for classifying the transient communication outages incurred by platooning vehicles into states. Furthermore, a state machine using these states to enable safe automated platooning is proposed that also defines the transitions between the states based on the nature and levels of transient connectivity and hazards. To achieve this, a graceful degradation and upgradation method is proposed, such that the platoon can remain fail-operational by adjusting, e.g., the automated controller and/or the inter-vehicle gaps based on the current communication quality. An emergency braking strategy is also proposed to enable a fast transition into a fail-safe state, should the platoon encounter a hazard. Rigorous simulation studies show that the proposed strategies enable fault-tolerant automated platooning also during transient connectivity.

Platooning is highly tractable for enabling fuel savings for autonomous and semi-autonomous cars and trucks. Safety concerns are one of the main impediments that need to be overcome before vehicle platoons can be deployed on ordinary roads despite their readily available technical feasibility. Simulation studies remain vital for evaluating platoon safety applications primarily due to the high cost of field tests. To this end, we present PlatoonSAFE, an open-source simulation tool that promotes the simulation studies of fault tolerance in platooning by enabling the monitoring of transient communication outages during runtime and assigning an appropriate performance level as a function of the instantaneous communication quality. In addition, PlatoonSAFE facilitates the simulation of several emergency braking strategies to evaluate their efficacy in transitioning a platoon to a fail-safe state. Furthermore, two Machine Learning (ML) models are integrated into PlatoonSAFE that can be employed as an onboard prediction tool in the platooning vehicles to facilitate online training of ML models and real-time prediction of communication, network, and traffic parameters. In this paper, we present the PlatoonSAFE structure, its features and implementation details, configuration parameters, and evaluation metrics required to evaluate the fault tolerance of platoon safety applications.

Audi's research car, the Audi A7 piloted driving concept, internally nicknamed Jack, is now driving more naturally. Jack knows how to autonomously perform all of its driving maneuvers on the expressway and has now learned how to show consideration for other road users by adapting its driving style to the situation at hand. To this end, it now passes trucks with a slightly wider lateral gap. It also signals upcoming lane changes by activating the turn signal and moving closer to the lane marking first, much like human drivers would do to indicate their intentions. The cooperative attitude of Jack is especially apparent when other vehicles want to merge into the lane, such as on an expressway. Based on the selected driving profile, the test car decides whether to accelerate or brake, depending on which is best suited to handling the traffic situation harmoniously for all road users.

Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC) method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications.