Autonomous vehicle (AV) is expected to reshape the future transportation system, and its decision-making is one of the most critical modules. Many current decision-making modules are designed scenario-by-scenario and thus are not capable of meeting high-level AV requirements for lack of scalability to cope with the diversity of drivers’ demands and the infinity of traffic elements. This paper surveys the decision-making design inspired by driver intelligence and environment reasoning with better scalability for future high-level AVs. It involves three aspects: human factors in driving, environment reasoning, and detailed decision methods. The current state of the art of these three sections is surveyed in this paper. The characteristics of distinguished drivers, their decision mechanism, and the factors influencing decision-making are reviewed first to learn from excellent drivers. Environment reasoning is introduced following as a three-layer structure consisting of restriction, interaction, and attention. The optimization-based decision-making algorithms are then reviewed from the aspect of optimization targets, frameworks, applied scenarios, and limitations. Inspired by the existing research on driver intelligence and environment reasoning, a promising decision-making framework is also introduced for high-level AV design.

Autonomous vehicles (AVs) are expected to reshape future transportation systems, and decision making is one of the critical modules toward high-level automated driving. To overcome those complicated scenarios that rule-based methods could not cope with well, data-driven decision-making approaches have aroused more focus. The datasets to be used in developing data-driven methods dramatically influence the performance of decision making; hence, it is necessary to have a comprehensive insight into the existing datasets. From the aspects of collection sources, driving data can be divided into vehicle-, environment-, and driver-related data. This study compares the state-of-the-art datasets of these three categories and summarizes their features, including sensors used, annotation, and driving scenarios. Based on the characteristics of the datasets, this survey also discusses potential applications of datasets on various aspects of AV decision making, assisting researchers in finding appropriate ones to support their own research. The future trends of AV dataset development are summarized.

Autonomous Vehicles have wide-ranging applications in off-road environments. Off-road vehicular scenes can be abstracted as multi-agent systems, and trajectory prediction is a critical process for context understanding. Agents compete and cooperate other to pursue their individual targets, which makes trajectories complicated and changeable. Hence, in order to derive precise predictions, it is necessary to reason how agents interact with each other. However, current prediction algorithms lack a unified and appropriate method for spatiotemporal reasoning. Previous methods mainly rely on vehicle-lane constraints to capture features, which is only applicable in structured environments. To address this issue, this paper proposes a novel off-road multi-agent trajectory prediction framework called SA-LSTM. This framework comprises situation awareness extraction and Long Short Term Memory (LSTM) prediction backbones. Based on the analysis of agents' movement patterns, we decompose actions into the maintenance of previous movements and the strategy variations in response to environment situations. In situation awareness extraction, risk field and pooling layers are applied to filter interpretable awareness. As for prediction backbones, proper LSTM networks are selected to adapt the task features. In short-term predictions, a residual mechanism is used to preserve physical inertia, while for long-term predictions autoregression process is applied. We also establish a multi-agent dataset based on human-manipulated chase-and-run tasks to train and validate the performance of SA-LSTM. Experiments show that compared with the best baseline model at each prediction length, SA-LSTM reduces the mean absolute error by 7.27%, 32.99%, and 28.07% at the prediction time of 0.6, 3.0, and 6.0 seconds, respectively, proving better prediction accuracy.

We have fabricated III–V gain region/Si waveguide hybrid lasers with an InP-based two-storied ridge structure using direct bonding technology. Continuous-wave operation at a temperature of 20 °C with a threshold current as low as 30 mA was obtained for a hybrid laser consisting of a Fabry–Perot cavity with facets formed on the Si waveguide, and it was achieved thanks to a high optical coupling efficiency of over 80% at the interface between the III–V gain region and the Si waveguide using a two-storied ridge structure and a taper waveguide. The wavelength tunable laser which is comprised of a Si cavity with a loop mirror and double ring filters and III–V gain regions with laser and semiconductor optical amplifier (SOA) sections was demonstrated by utilizing this hybrid structure. It exhibited a wide wavelength tuning range of 48.6 nm and an optical amplification of 10 dB by SOA.

One critical difficulty to high‐level automated driving is the decision‐making process of automated vehicles in complicated traffic environments, especially in situations mixed of pedestrians and vehicles. This paper proposes a differentiated decision‐making algorithm to promote passing capability and efficiency in mixed traffic conditions. First, the behavioural characteristic of pedestrians, denoted as the pedestrian feature index, is estimated by a multi‐layer perception module input with quantitative analysis of pedestrian action. Based on estimation results, the decision algorithm merges pedestrian feature index into intelligent driver model and adjusts corresponding parameters, which used to be unchangeable so that the ego‐vehicle can make differential decisions according to various pedestrian features. Validation on the PIE dataset shows that the accuracy of pedestrian feature estimation is ensured. A simulation scenario is established utilizing cellular automata, and the results indicate that the proposed decision‐making algorithm can greatly improve passing efficiency under safety and manoeuvrability prerequisite.