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Cellular Vehicle-to-Everything (C-V2X) is considered an enabler for fully automated driving. It can provide the needed information about traffic situations and road users ahead of time compared to the onboard sensors which are limited to line-of-sight detections. This work presents the investigation of the effectiveness of utilizing the C-V2X technology for a valet parking collision mitigation feature. For this study a LiDAR was mounted at the FEV North America parking lot in a hidden intersection with a C-V2X roadside unit. This unit was used to process the LiDAR point cloud and transmit the information of the detected objects to an onboard C-V2X unit. The received data was provided as input to the path planning and controls algorithms so that the onboard controller can make the right decision while approaching the hidden intersection. FEV’s Smart Vehicle Demonstrator was utilized to test the C-V2X setup and the developed algorithms.

Autonomous farming has gained a vast interest due to the need for increased farming efficiency and productivity as well as reducing operating cost. Technological advancement enabled the development of Autonomous Driving (AD) features in unstructured environments such as farms. This paper discusses an approach of utilizing satellite images to estimate the drivable areas of agriculture fields with the aid of LiDAR sensor data to provide the necessary information for the vehicle to navigate autonomously. The images are used to detect the field boundaries while the LiDAR sensor detects the obstacles that the vehicle encounters during the autonomous driving as well as its type. These detections are fused with the information from the satellite images to help the path planning and control algorithms in making safe maneuvers. The image and point cloud processing algorithms were developed in MATLAB®/C++ software and implemented within the Robot Operating System (ROS) middleware.

Although SAE level 5 autonomous vehicles are not yet commercially available, they will need to be the most intelligent, secure, and safe autonomous vehicles with the highest level of automation. The vehicle will be able to drive itself in all lighting and weather conditions, at all times of the day, on all types of roads and in any traffic scenario. The human intervention in level 5 vehicles will be limited to passenger voice commands, which means level 5 autonomous vehicles need to be safe and capable of recovering fail operational with no intervention from the driver to guarantee the maximum safety for the passengers. In this paper a LiDAR-based fail-safe emergency maneuver system is proposed to be implemented in the level 5 autonomous vehicle.