Connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC) create new opportunities for highway congestion mitigation. Traditional practice relies on Eulerian variable speed limits (VSL) which regulate traffic through roadside signs, but suffer from infrequent updates and limited driver compliance. Recent research explored Lagrangian strategies that directly control individual vehicles, offering high reactivity and compliance, yet in realistic multi-lane settings they depend on drivers' latent lane-change intentions, making robust vehicle-level decisions difficult. Hence, we propose an Eulerian control system optimized through reinforcement learning, that (i) leverages ACC for reactivity and compliance, and (ii) obviates dependence on latent driver intentions by regulating aggregate density near bottlenecks, crucially via headway commands rather than speed commands. We evaluate two variants of our system, time-headway and distance-headway control, in large-scale simulations across a range of traffic conditions. Both variants outperform baselines, improving traffic flow by up to 10.6% over human traffic and 6.7% over traditional VSL. To strengthen evaluation, we propose a novel boundary-aware speed metric addressing a recognized flaw in simulation studies with dynamic vehicle entry and exit. The empirical results, together with our emphasis on deployable system design, suggest a path towards practical, safe, and scalable highway congestion mitigation.
This study introduces a novel control framework for adaptive cruise control (ACC) in automated driving, leveraging neural networks and physics-informed constraints. As automated vehicles (AVs) adopt advanced features like ACC, transportation systems are becoming increasingly intelligent and efficient. However, existing AV control strategies primarily focus on optimizing the performance of individual vehicles or platoons, often neglecting their interactions with human-driven vehicles (HVs) and the broader impact on traffic flow. This oversight can exacerbate congestion and reduce overall system efficiency. To address this critical research gap, we propose a neural network-based, socially compliant AV control framework that incorporates social value orientation (SVO). This framework enables AVs to account for their influence on HVs and traffic dynamics. By leveraging AVs as mobile traffic regulators, the proposed approach promotes adaptive driving behaviors that reduce congestion, improve traffic efficiency, and lower energy consumption. Numerical results demonstrate the effectiveness of the proposed method in adapting to varying traffic conditions, thereby enhancing system-wide efficiency. Specifically, when the AV's control mode shifts from prioritizing its own energy conservation to optimizing collective traffic flow efficiency, the controlled AV proactively adapts its acceleration profile. This prosocial behavior yields at least a 38.39% improvement in the average speed of downstream vehicles and effectively dampens traffic oscillations, demonstrating significant enhancements in system-wide dynamics. The implementation code is available https://github.com/lilab2024/Learn2Drive-SVO_v1.git.
Accurate simulation is crucial for autonomous driving development, yet capturing real-world traffic complexity remains challenging. Existing simulators that rely on predefined rules or static data playback struggle with dynamic traffic. CRITICAL uses real traffic data and a large language model (LLM) to adjust the initial simulation configuration, but the simulated distribution still diverges from real traffic as the rollout evolves. We propose REARL, a closed-loop simulation enhancement framework that integrates real traffic data with LLMs. Real traffic data are clustered, and each cluster center is used as a representative scenario that provides typical real-world traffic patterns for the LLM. A timed sliding-window detector then monitors discrepancies in vehicle speed distribution and mean spacing between pairs of vehicles. If a metric exceeds a threshold, the LLM adjusts vehicle decision-making; otherwise the existing controller is kept. The LLM also selects a matching real vehicle from a traffic snapshot and modulates the simulated vehicle with reference to that real action. In a controlled HighD highway setting, compared with the CRITICAL baseline and a PPO-based learning baseline, REARL reduces the Hellinger distance for speed distributions to 0.3067 and the MAPE for mean spacing to 0.8371, while achieving a time headway (THW) of 22.8575 and a lane change rate of 0.0708.
Urban traffic congestion significantly increases fuel consumption, greenhouse gas emissions, and commuter delays, resulting in substantial economic losses and environmental harm in modern cities. Traditional traffic signal control strategies such as fixed-time scheduling, actuated control, and reinforcement learning (RL)-based methods, offer different degrees of adaptability; however, RL-based methods can require extensive retraining, careful reward design, and substantial simulation data when transferred across networks or demand regimes. To address these challenges, we propose HiLLTS, an LLM-guided traffic signal control framework that employs a hierarchical three-layer architecture consisting of a central coordination agent, a district layer and multiple cluster-level intersection agents. Experimental results demonstrate consistent improvements in both congestion and environmental performance. Compared with the strongest non-LLM baseline in each scenario, HiLLTS reduces average waiting time by 36.73% under the low-congestion scenario and 14.71% under the high-congestion scenario, while reducing average CO2 emissions by 7.87% and 8.57%, respectively. Larger gains are observed against weaker baselines: under low congestion, HiLLTS achieves reductions of up to 18.00% in emissions and 62.07% in waiting time relative to Fixed-Time control; under high congestion, reductions of up to 28.89% in emissions and 40.36% in waiting time are observed relative to Max Pressure. The ablation study further validates the contribution of LLM-guided coordination over rule-based control