In recent years, there has been a growing emphasis on developing automated systems to enhance traffic safety, particularly in the detection of dilemma zones (DZ) at intersections. This study focuses on the automated detection of DZs at roundabouts using trajectory forecasting, presenting an advanced system with perception capabilities. The system utilizes a modular, graph-structured recurrent model that predicts the trajectories of various agents, accounting for agent dynamics and incorporating heterogeneous data such as semantic maps. This enables the system to facilitate traffic management decision-making and improve overall intersection safety. To assess the system's performance, a real-world dataset of traffic roundabout intersections was employed. The experimental results demonstrate that our Superpowered Trajectron++ system exhibits high accuracy in detecting DZ events, with a false positive rate of approximately 10%. Furthermore, the system has the remarkable ability to anticipate and identify dilemma events before they occur, enabling it to provide timely instructions to vehicles. These instructions serve as guidance, determining whether vehicles should come to a halt or continue moving through the intersection, thereby enhancing safety and minimizing potential conflicts. In summary, the development of automated systems for detecting DZs represents an important advancement in traffic safety. The Superpowered Trajectron++ system, with its trajectory forecasting capabilities and incorporation of diverse data sources, showcases improved accuracy in identifying DZ events and can effectively guide vehicles in making informed decisions at roundabout intersections.

Deep neural network-based methods have been proved to achieve outstanding performance on object detection and classification tasks. Deep neural networks follow the ``deeper model with deeper confidence'' belief to gain a higher recognition accuracy. However, reducing these networks' computational costs remains a challenge, which impedes their deployment on embedded devices. For instance, the intersection management of Connected Autonomous Vehicles (CAVs) requires running computationally intensive object recognition algorithms on low-power traffic cameras. This dissertation aims to study the effect of a dynamic hardware and software approach to address this issue. Characteristics of real-world applications can facilitate this dynamic adjustment and reduce the computation. Specifically, this dissertation starts with a dynamic hardware approach that adjusts itself based on the toughness of input and extracts deeper features if needed. Next, an adaptive learning mechanism has been studied that use extracted feature from previous inputs to improve system performance. Finally, a system (ARGOS) was proposed and evaluated that can be run on embedded systems while maintaining the desired accuracy. This system adopts shallow features at inference time, but it can switch to deep features if the system desires a higher accuracy. To improve the performance, ARGOS distills the temporal knowledge from deep features to the shallow system. Moreover, ARGOS reduces the computation furthermore by focusing on regions of interest. The response time and mean average precision are adopted for the performance evaluation to evaluate the proposed ARGOS system.