Autonomous driving often suffer from a decoupled feedback loop between prediction and planning. While prediction losses focus on the accuracy of surrounding agents, planning losses typically imitate recorded Autonomous Vehicles’ (AVs) trajectories that may contain suboptimal or aggressive behaviors, leading to unstable interactions in mixed traffic. This paper presents HybridLoss, an adaptive planning-oriented objective that unifies prediction and planning through planner-in-the-loop supervision and interaction-aware consistency. HybridLoss integrates an adaptive motion-planning module which replaces ground-truth targets with optimized reference trajectories, and a multi-term loss combining prediction, adaptive planning, safety potential, and social force objectives. Evaluations on the INTERACTION dataset indicate that HybridLoss significantly outperforms strong baselines. Beyond standard metric improvements—reducing ADE/FDE from 1.36/1.64 m to 1.11/1.36 m and collision rates from 0.19% to 0.11%—extensive stress-testing reveals superior system maturity. First, HybridLoss exhibits the highest robustness under input perturbations, maintaining the lowest planning deviation and endpoint standard deviation. Second, it demonstrates strong generalization, maintaining stable success rates (87.7%) in unseen scenarios with high computational efficiency (64.3 Hz). Third, multi-objective analysis confirms that HybridLoss achieves the optimal Pareto trade-off between efficiency, safety, and comfort, avoiding the speed-safety collapse seen in baseline methods. Finally, social force evaluations highlight that HybridLoss fosters implicit cooperation, achieving higher yield rates and reduced conflict indices while maintaining safe interaction buffers. These results validate HybridLoss as a robust, socially compliant, and adaptive solution for end-to-end driving.

This paper employs lateral and longitudinal trajectory planning to generate candidate trajectories and discards those that do not satisfy the constraints imposed by single-vehicle conditions. Next, a collaborative trajectory combination set for multiple vehicles is derived from the candidate trajectories, with multi-vehicle constraints applied to eliminate combinations that fail to meet the required conditions. The objective function for each candidate trajectory set is first calculated using a single-vehicle objective function, after which a multi-vehicle objective function based on the Kaldor-Hicks improvement principle is constructed. Finally, the paper introduces an improved particle swarm optimization method for multi-vehicle collaborative trajectory planning. The results demonstrate that the dynamic spatiotemporal occupancy growth rate, under varying planning times and frequencies, is at least 19%. Furthermore, the proposed algorithm ensures efficient allocation of travel resources, preventing competition among vehicles that could compromise system feasibility. When verified with HighD trajectory data, the algorithm not only delivers superior optimization results but also exhibits lower standard deviations in dynamic spatiotemporal occupancy and speed compared to real-world data. Finally, the algorithm’s superiority in real-time decision-making and stability is confirmed. Note to Practitioners—In the context of mixed traffic comprising both autonomous and human-driven vehicles, this paper tackles the challenge of coordinating autonomous vehicles to improve traffic efficiency and safety in real-time environments. It presents a promising approach to enhancing cooperative behavior in complex scenarios by integrating real-time data streams to optimize adaptability and ensure equitable driving efficiency across different vehicle types.

Urban public transportation is a complex and open system integral to urban mobility. Its operation is often disrupted by various random factors, necessitating robust scheduling solutions. This study develops a bus robust scheduling model based on mixed-integer linear programming to enhance system resilience. First, an arterial signal coordination model is proposed for mixed traffic environments, enabling autonomous public transport vehicles to traverse intersections without stopping. Second, a demand-deterministic bus scheduling model is constructed, integrating timetables, trajectories, and origin-destination transfer schemes to balance passenger waiting time fairness and efficiency. Third, to address stochastic passenger demand during actual operations, a robust bus scheduling model is developed by incorporating robust constraints. Numerical experiments demonstrate that the demand-deterministic model generates optimal scheduling schemes when passenger demand remains within bus capacity. However, when passenger demand exceeds capacity, the demand-deterministic model becomes infeasible. In such scenarios, the robust scheduling model produces feasible schemes, albeit with reduced optimization, and its robustness can be tuned by adjusting model parameters. Additionally, practical management insights are provided for real-world applications.

In this study, we introduce a novel hybrid trajectory planning algorithm for autonomous driving, specifically designed to mitigate the risks posed by spoofing attacks on Connected and Autonomous Vehicles (CAVs). The research begins by assessing the safety implications of attacks and developing an adaptive safety model that is grounded in the fundamental assessment of state and decision data. This model incorporates the establishment of a posterior probability distribution for decision data, rooted in the pre-existing prior distribution but adjusted to account for the influence of spoofing attacks. The adjustment is achieved through Bayesian maximum posterior estimation, thereby refining the model to better adapt to potential threats. The adaptive safety model is then optimized dynamically, taking into consideration a set of indices—safety, comfort, and efficiency—that are critical to trajectory planning. In the subsequent phase, we introduce a composite trajectory planning algorithm that integrates a lateral trajectory selection sampling method with a longitudinal trajectory optimization approach. The adaptive safety model is seamlessly integrated into the trajectory planning process, influencing target position selection, the setting of constraints, and the formulation of the optimization objective function. The results demonstrate that the algorithm effectively limits the average standard deviation of lateral displacement to 0.5851 and achieves a significant increase in longitudinal speed growth rate, by up to 7.30%, surpassing the performance of benchmark algorithms. The proposed solution consistently delivers optimal safety and efficiency across various scenarios and under different parameter conditions.

Modern complexities associated with an arterial traffic makes existing safety prediction methods insufficient to meet desired standards required by recent developmental needs. This paper proposes an enhanced active safety prediction method based on big-data approach and Stacked AutoEncoder-Gated Recurrent Unit. Firstly, the big-data technology is used to construct a dynamic identification model to recognize real-time operation state and risk state. Secondly, the Stacked AutoEncoder-Gated Recurrent Unit is used to predict a level of safety based on associated recognition results. This paper uses data from working days of Sunset Boulevard, California, from January$1^{\mathrm{st}}$, 2020, to February$28^{\mathrm{th}}$, 2020. The results of analysis show that the accuracy of the proposed dynamic recognition model reaches 98.92%, which is better than existing models such as random forest, K-nearest neighbor, and naïve Bayes models. In addition, it is found that the Stacked AutoEncoder-Gated Recurrent Unit can achieve a prediction accuracy of 95.157% and has significant advantages in terms of efficiency. The proposed methods will provide feasible solutions for actively monitoring safety levels.