Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles

Learning-based approaches to autonomous vehicle planners have the potential to scale to many complicated real-world driving scenarios by leveraging huge amounts of driver demonstrations. However, prior work only learns to estimate a single planning trajectory, while there may be multiple acceptable plans in real-world scenarios. To solve the problem, we propose an interpretable neural planner to regress a heatmap, which effectively represents multiple potential goals in the bird's-eye view of an autonomous vehicle. The planner employs an adaptive Gaussian kernel and relaxed hourglass loss to better capture the uncertainty of planning problems. We also use a negative Gaussian kernel to add supervision to the heatmap regression, enabling the model to learn collision avoidance effectively. Our systematic evaluation on the Lyft Open Dataset across a diverse range of real-world driving scenarios shows that our model achieves a safer and more flexible driving performance than prior works

Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing

Generating safety-critical scenarios is essential for testing and verifying the safety of autonomous vehicles. Traditional optimization techniques suffer from the curse of dimensionality and limit the search space to fixed parameter spaces. To address these challenges, we propose a deep reinforcement learning approach that generates scenarios by sequential editing, such as adding new agents or modifying the trajectories of the existing agents. Our framework employs a reward function consisting of both risk and plausibility objectives. The plausibility objective leverages generative models, such as a variational autoencoder, to learn the likelihood of the generated parameters from the training datasets; It penalizes the generation of unlikely scenarios. Our approach overcomes the dimensionality challenge and explores a wide range of safety-critical scenarios. Our evaluation demonstrates that the proposed method generates safety-critical scenarios of higher quality compared with previous approaches

Investigation of the stability of the anti-islanding detection in multi-DGs system

U radu je predstavljen poboljšani model multi-DGs mikro rešetki za analizu stabilnosti sustava tijekom vezivanja s rešetkom. DGs u sustavu opremljeni su Sandia frequency shift (SFS) shemom kao načinom anti-islanding zaštite. Uvođenjem dužine linije distribucijske mreže, pozitivnog porasta povratne sprege SFSa i distribuiranog dovoda energije, parametri izlazne snage za poboljšanje matematičkog modela mikro energetskih rešetki uspostavljeni su u tri vrste parametara i odnosu između margine stabilnosti mikro energetske rešetke za postizanje stabilnosti sustava praga dužine linije energetske mreže, i stabilnosti granične vrijednosti napona izlazne snage distribuirane istosmjerne struje. Taj postupak omogućuje projektantima i inženjerima obnovljivih energetskih sustava optimiziranje sustava i osiguranje stabilnosti. Konačno, uzimajući u obzir nekoliko potvrđivanja simulacija, u radu se daje poboljšani model koji može utjecati na aktualnu implementaciju analize distribuirane mikro energetske rešetke, te se tako može donijeti zaključak o stabilnosti kritičkog praga parametara sustava. Na temelju tih analiza slučaja, pokazalo se da je stabilnost sustava vrlo važna za stabilnost mikrorešetki mnogih distribuiranih multi-DGs, koji su korisni za projektiranje i implementaciju novih energetskih sustava.This paper presents an improved model of multi-DGs microgrids for analysing system stability during grid-connections. The DGs-in the system are equipped with the Sandia frequency shift (SFS) scheme as an anti-islanding protection technique. By introducing a distribution network line length, SFS positive feedback gain and distributed power supply, power output parameters to improve the micro power grid mathematical model are established in three kinds of parameters and the relationship between micro power grid stability margin, to obtain stability of the system of power line length threshold, and stability of the distributed power dc output voltage threshold. This process allows the designers and engineers of renewable energy systems to optimize the system and ensure stability. Finally, in view of the several common simulation validations, this paper puts forward an improved model that can affect actual implementation of distributed micro power grid analysis, whereby the stability of the system parameters’ critical threshold may be deduced. Based on these case studies, system stability is shown to be very important to the stability of many distributed multi-DGs microgrids, which are useful for the design and implementation of new energy systems