## 5 Passenger Vehicle Demonstrator for Low Speed Autonomy (PVLSA)  
While fully autonomous traffic is still far away in the future, most OEMs are striving for mass deployment of SAE level-2 vehicles [9] with a limited proportion of conditional and highly automated driving (i.e., SAE level-3 and level-4 [9]) operations expected in the next decade [34]. In this respect, one of the first applications or use-cases of automated driving, starting from automated vehicles with SAE level-2 autonomy, is the automated parking and retrieval operations [35]. The main reasons for the attractiveness of this use case are as follows:  
1. During a typical parking manoeuvre, vehicle dynamics are mostly negligible, and motion is predominantly kinematic, simplifying control and planning tasks. 2. The dynamic environment does not typically involve rapid changes (compared to on-road driving), and in most cases, can even be considered as quasi-static. Please  
Figure 10: Egomotion planning using Autoware. Figure 9: Object detection and mapping subsystem of the EDI DbW car. note that this does not exclude edge use-cases, possibly involving highly-dynamic scenarios, which are, however, outside the scope of this study. 3. Vehicle parking and retrieval are usually considered a burden for many vehicle owners and thus automated valet solutions are likely to have more interest and user acceptance than general automated driving operations. Motivated by these, a dedicated demonstrator named \"Passenger Vehicle for Low-Speed Autonomy\" (PVLSA) was developed in the scope of the Prystine project to implement parking use-cases. While it is not a new application area, the implementation involved the development of dedicated environmental perception algorithms as well as the path planning for parking. The perception algorithms utilize AI algorithms with superfluous combinations of RADAR, LIDAR and camera. The focus of the demonstrator was, however, parking use cases in cluttered environments, where different path planning algorithms were implemented and compared using a set of KPIs. The baseline for PVLSA demonstrator is the existing SAE level-2 and level-3 vehicles with parking assistance systems. The best SotA for this is the \"Autopark\" and \"Smart Summon\" systems from Tesla, which are designed respectively for parallel parking assistance and automated retrieval of the vehicle in parking lots. These, however, are based on camera perception alone and are reportedly not working up to the customer expectations. The PVLSA demonstrator, on the other hand, aimed to demonstrate similar implementations of automated parking utilizing sensor fusion approaches (i.e., perception using cameras, LIDAR and RADAR) to improve the robustness of the decision-making process. As an environment of interest, a typical parking lot scenario was assumed for this purpose, and the set goal was to park in predefined spots provided as user input, utilizing backward and forward parking manoeuvres. The main contribution of the PVLSA demonstrator in this respect is the development and benchmarking of a high-level trajectory planner based on RRT (rapidly exploring random tree) family of search-based algorithms [36] for efficient navigation and automated parking in cluttered environments. The RRT-based trajectory planner block requires an environment map as well as start and goal positions, based on which path planning is performed. The RRT-based planner calculates a path or generates an error message, which indicates whether a goal state collision exists or if no path is found within the specified time. For the tracking controller, a way-point list with a pose and a set velocity is required.