Current evaluation methods for autonomous driving prediction models rely heavily on simplistic metrics such as Average Displacement Error (ADE) and Final Displacement Error (FDE). While these metrics offer basic performance assessments, they fail to capture the nuanced behavior of prediction modules under complex, interactive, and safety-critical driving scenarios. For instance, existing benchmarks do not distinguish the influence of nearby versus distant agents, nor systematically test model robustness across varying multi-agent interactions. This paper addresses this critical gap by proposing a novel testing framework that evaluates prediction performance under diverse scene structures, saying, map context, agent density and spatial distribution. Through extensive empirical analysis, we quantify the differential impact of agent proximity on target trajectory prediction and identify scenario-specific failure cases that are not exposed by traditional metrics. Our findings highlight key vulnerabilities in current state-of-the-art prediction models and demonstrate the importance of scenario-aware evaluation. The proposed framework lays the groundwork for rigorous, safety-driven prediction validation, contributing significantly to the identification of failure-prone corner cases and the development of robust, certifiable prediction systems for autonomous vehicles.

Reinforcement learning encounters challenges in various environments related to robustness and explainability. Traditional Q-learning algorithms cannot effectively make decisions and utilize the historical learning experience. To overcome these limitations, we propose Cognitive Belief-Driven Q-Learning (CBDQ), which integrates subjective belief modeling into the Q-learning framework, enhancing decision-making accuracy by endowing agents with human-like learning and reasoning capabilities. Drawing inspiration from cognitive science, our method maintains a subjective belief distribution over the expectation of actions, leveraging a cluster-based subjective belief model that enables agents to reason about the potential probability associated with each decision. CBDQ effectively mitigates overestimated phenomena and optimizes decision-making policies by integrating historical experiences with current contextual information, mimicking the dynamics of human decision-making. We evaluate the proposed method on discrete control benchmark tasks in various complicate environments. The results demonstrate that CBDQ exhibits stronger adaptability, robustness, and human-like characteristics in handling these environments, outperforming other baselines. We hope this work will give researchers a fresh perspective on understanding and explaining Q-learning.

We present ChatScene, a Large Language Model (LLM)-based agent that leverages the capabilities of LLMs to generate safety-critical scenarios for autonomous vehicles. Given unstructured language instructions, the agent first generates textually described traffic scenarios using LLMs. These scenario descriptions are subsequently broken down into several sub-descriptions for specified details such as behaviors and locations of vehicles. The agent then distinctively transforms the textually described sub-scenarios into domain-specific languages, which then generate actual code for prediction and control in simulators, facilitating the creation of diverse and complex scenarios within the CARLA simulation environment. A key part of our agent is a comprehensive knowledge retrieval component, which efficiently translates specific textual descriptions into corresponding domain-specific code snippets by training a knowledge database containing the scenario description and code pairs. Extensive experimental results underscore the efficacy of ChatScene in improving the safety of autonomous vehicles. For instance, the scenarios generated by ChatScene show a 15% increase in collision rates compared to state-of-the-art baselines when tested against different reinforcement learning-based ego vehicles. Furthermore, we show that by using our generated safety-critical scenarios to fine-tune different RL-based autonomous driving models, they can achieve a 9% reduction in collision rates, surpassing current SOTA methods. ChatScene effectively bridges the gap between textual descriptions of traffic scenarios and practical CARLA simulations, providing a unified way to conveniently generate safety-critical scenarios for safety testing and improvement for AVs.

High-definition roads are an essential component of realistic driving scenario simulation for autonomous vehicle testing. Roundabouts are one of the key road segments that have not been thoroughly investigated. Based on the geometric constraints of the nearby road structure, this work presents a novel method for procedurally building roundabouts. The suggested method can result in roundabout lanes that are not perfectly circular and resemble real-world roundabouts by allowing approaching roadways to be connected to a roundabout at any angle. One can easily incorporate the roundabout in their HD road generation process or use the standalone roundabouts in scenario-based testing of autonomous driving.

Path tracking system plays a key technology in autonomous driving. The system should be driven accurately along the lane and be careful not to cause any inconvenience to passengers. To address such tasks, this paper proposes hybrid tracker based optimal path tracking system. By applying a deep learning based lane detection algorithm and a designated fast lane fitting algorithm, this paper developed a lane processing algorithm that shows a match rate with actual lanes with minimal computational cost. In addition, three modified path tracking algorithms were designed using the GPS based path or the vision based path. In the driving system, a match rate for the correct ideal path does not necessarily represent driving stability. This paper proposes hybrid tracker based optimal path tracking system by applying the concept of an observer that selects the optimal tracker appropriately in complex road environments. The driving stability has been studied in complex road environments such as straight road with multiple 3-way junctions, roundabouts, intersections, and tunnels. Consequently, the proposed system experimentally showed the high performance with consistent driving comfort by maintaining the vehicle within the lanes accurately even in the presence of high complexity of road conditions. Code will be available in https://github.com/DGIST-ARTIV.

Musical adventure No Straight Roads certainly got my foot tapping, albeit more due to frustration than the game's admittedly catchy tunes. Set in the music-obsessed metropolis of Vinyl City, No Straight Roads sees you play as two wannabe rock stars fighting against the dictatorship of NSR records, which not only mediates the city's music tastes, but also controls its power supply. No Straight Roads has what it takes to be a pop sensation, namely a strong sense of style and a good ear. Vinyl City is a dazzling neon sprawl divided into multiple districts, from the kawaii-colours and anime billboards of Akusuka, to the leafy groves and neoclassical architecture of Natura. These districts are controlled by powerful pop stars you need to face in battle, with victory bringing you one step closer to NSR's glittering skyscraper.

Creating driverless cars capable of humanlike reasoning is a long-standing pursuit of companies like Waymo, GM's Cruise, Uber, and others. Intel's Mobileye proposes a mathematical model -- the Responsibility-Sensitive Safety (RSS) -- it describes as a "common sense" approach to on-the-road decision-making that codifies good habits like giving other cars the right of way. For its part, Nvidia is actively developing Safety Force Field, a decision-making policy in a motion-planning stack that monitors unsafe actions by analyzing real-time sensor data. Now, a team of MIT scientists are investigating an approach that leverages GPS-like maps and visual data to enable autonomous cars to learn human steering patterns, and to apply the learned knowledge to complex planned routes in previously unseen environments. Their work -- which will be presented at the International Conference on Robotics and Automation in Long Beach, California next month -- builds on end-to-end navigation systems architected by Daniel Rus, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

With aims of bringing more human-like reasoning to autonomous vehicles, MIT researchers have created a system that uses only simple maps and visual data to enable driverless cars to navigate routes in new, complex environments. Human drivers are exceptionally good at navigating roads they haven't driven on before, using observation and simple tools. We simply match what we see around us to what we see on our GPS devices to determine where we are and where we need to go. In every new area, the cars must first map and analyze all the new roads, which is very time consuming. The systems also rely on complex maps -- usually generated by 3-D scans -- which are computationally intensive to generate and process on the fly.