Browse Topic: Trajectory control
Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints. Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and
As the autonomy of ADAS features are moving from SAE level 0 autonomy to SAE level 5 autonomy of operation, reliance on AI/ML based algorithms in ADAS critical functions like perception, fusion and path planning are increasing predominantly. AI/ML based algorithms offer exceptional performance of the ADAS features, at the same time these advanced algorithms also bring in safety challenges as well. This paper explores the functional safety aspects of AI/ML based systems in ADAS functions like perception, object fusion and path planning, by discussing the safety requirements development for AI/ML systems, dataset safety life cycle, verification and validation of AI systems, and safety analysis used for AI systems. Among all the safety aspects listed above, emphasis is put on dataset safety lifecycle as that is not only the most important element for training ML based algorithms for ADAS usage, but also the most cumbersome and expensive. The safety characteristics associated with dataset
The application trend of automated driving is gaining significant concern, making it increasingly crucial to validate automated driving within the stochastic simulated traffic flow environment from both time and cost perspectives. The stochastic traffic flow model attempts to encapsulate the variability inherent in traffic conditions through a stochastic process. This approach is particularly important as it accounts for the unpredictable nature of traffic, which is often not fully captured by traditional deterministic testing scenarios. However, while stochastic traffic flow models have made strides in simulating the behavior of traffic participants, there remains a significant oversight in the simulation of vehicles’ driving trajectories, leading to unrealistic portrayals of their behaviors. The trajectories of vehicles are a critical component in the overall behavior of traffic flow, and their accurate representation is essential for the simulation to reflect real-world driving
Hypersonic platforms provide a challenge for flight test campaigns due to the application's flight profiles and environments. The hypersonic environment is generally classified as any speed above Mach 5, although there are finer distinctions, such as “high hypersonic” (between Mach 10 to 25) and “reentry” (above Mach 25). Hypersonic speeds are accompanied, in general, by a small shock standoff distance. As the Mach number increases, the entropy layer of the air around the platform changes rapidly, and there are accompanying vortical flows. Also, a significant amount of aerodynamic heating causes the air around the platform to disassociate and ionize. From a flight test perspective, this matters because the plasma and the ionization interfere with the radio frequency (RF) channels. This interference reduces the telemetry links' reliability and backup techniques must be employed to guarantee the reception of acquired data. Additionally, the flight test instrumentation (FTI) package needs
Items per page:
50
1 – 50 of 238