Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. The paper focuses on testing for verification and validation purposes at the interface between perception and planning, and structures the analysis along the three axes (1) test criteria and metrics, (2) test scenarios, and (3) reference data. Furthermore, the analyzed literature includes related safety standards, safety-independent perception algorithm benchmarking, and sensor modeling. It is found that the realization of safety-oriented perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved.

In recent years, camera- and lidar-based 3D object detection has achieved great progress. However, the related researches mainly focus on normal illumination conditions; the performance of their 3D detection algorithms will decrease under low lighting scenarios such as in the night. This work attempts to improve the fusion strategies on 3D vehicle detection accuracy in multiple lighting conditions. First, distance and uncertainty information is incorporated to guide the “painting” of semantic information onto point cloud during the data preprocessing. Moreover, a multitask framework is designed, which incorporates uncertainty learning to improve detection accuracy under low-illumination scenarios. In the validation on KITTI and Dark-KITTI benchmark, the proposed method increases the vehicle detection accuracy on the KITTI benchmark by 1.35% and the generality of the model is validated on the proposed Dark-KITTI dataset, with a gain of 0.64% for vehicle detection.

Real driving scenarios, due to occlusions and disturbances, provide disordered and noisy measurements, which makes the task of multi-object tracking quite challenging. Conventional approach is to find deterministic data association; however, it has unstable performance in high clutter density. This paper proposes a novel probabilistic tracklet-enhanced multiple object tracker (PTMOT), which integrates Poisson multi-Bernoulli mixture (PMBM) filter with confidence of tracklets. The proposed method is able to realize efficient and robust probabilistic association for 3D multi-object tracking (MOT) and improve the PMBM filter’s continuity by smoothing single target hypothesis with global hypothesis. It consists of two key parts. First, the PMBM tracker based on sets of tracklets is implemented to realize probabilistic fusion of disordered measurements. Second, the confidence of tracklets is smoothed through a smoothing-while-filtering approach. Extensive MOT tests on nuScenes tracking dataset demonstrate that the proposed method achieves superior performance in different modalities.

With the continuous development of automotive intelligent networking and autonomous driving technologies, the number of in-vehicle electronic systems and applications is increasing rapidly. This change increases the amount of data to be transmitted in the vehicle and puts forward further requirements of higher speed and safety for in-vehicle communication. Traditional vehicle bus technologies are no longer sufficient to meet today’s high-speed transmission requirements, in which copper cables are used extensively, resulting in serious electromagnetic interference (EMI). Vehicle optical fiber communication technology, besides greatly improving the data transmission rate, has the advantages of anti-EMI, reducing cable space and vehicle mass. This paper first presents the motivation of applying vehicle optical fiber communication technology and reviews the development history of vehicle optical fiber communication technology. Then, the paper researches the development trend of automotive electrical and electronic architecture (EEA), from distributed EEA to domain centralized EEA and zone-oriented EEA. Based on the discussion of the development trend of automotive EEA, an EEA based on vehicle optical fiber communication technology is proposed. Finally, the key points and future directions of vehicle optical fiber communication technology research are highlighted, including vehicle multi-mode optical fiber technology, vehicle optical fiber network protocol, and topology.

This paper presents a comprehensive evaluation of system interactions in a battery electric vehicle caused by temperature sensitivity of permanent magnet synchronous machines (PMSM). An analytical model of a PMSM considering iron losses and thermal impact is implemented on a field programmable gate array suitable for hardware-in-the-loop testing. By the presented virtual prototyping approach, different machine characteristics defined by the design are used to parameterize the analytical model. The investigated temperature effect is understood as an interacting influence between machine characteristics and control, which are investigated in terms of torque generation, voltage utilization and efficiency under closed-loop condition in a vehicle environment. In particular, using a surface permanent magnet rotor and an interior permanent magnet rotor, the performance of both machine designs is analyzed by varying temperature-adjusted feedforward control strategies on the basis of a driving cycle from a racetrack. The comparison shows that the machine design with surface-mounted magnets is associated with higher temperature sensitivity. In this case, the temperature consideration in the feedforward control provides a 14% loss reduction in closed-loop vehicle test operation. It can be summarized that the electromagnetic torque is less sensitive to a temperature variation with increasing reluctance. The presented development approach demonstrates the impact of interactions in electric powertrains without the need of real prototypes.

With the continuous improvement of automated driving technology, how to evaluate the performance of an automated driving system is attracting more and more attention. Meanwhile, with the creation of scenario-based test methods, the traditional evaluation index based on a single test can no longer meet the requirements of high-level safety verification for automated driving system, and the performance evaluation of such a system in logical scenarios will be the mainstream. Based on the scenario-based test method and Turing test theory, a performance evaluation method for an automated driving system in the whole parameter space of a logical scenario is proposed. The logical scenario parameter space is partitioned according to the risk degree of concrete scenario, and the evaluation process in different zones are determined. Subsequently, the anthropomorphic index in the safe zone and the collision-avoidance index in the danger zone are defined by comparing test results of human driving and ideal vehicle motion. Taking front vehicle low-speed and cut-out scenarios as examples, two automated driving algorithms are tested in the virtual environment, and the test results are evaluated both by the proposed method and by human observation. The results show that the results of the proposed method are consistent with the subjective feelings of humans; additionally, it can be applied to scenario-based tests and the verification process of an automated driving system.

Updating high-definition maps is imperative for the safety of autonomous vehicles. However, positional changes in lane lines are hard to be detected in a timely manner due to a limited number of expensive surveying vehicles over a large geographic area. Herein, a novel method is proposed to detect the geometric changes of lane lines using low-cost sensors, such as consumer-grade global navigation satellite system (GNSS) hardware receivers and cameras. The proposed framework geometric change detection using low-cost sensors (GCD-L) and algorithm change segment compare (CSC), which are based on the lane width between the curb line and the adjacent leftmost lane line, can perceive the positional changes of the leftmost lane line on highway and expressway roads. The effectiveness of the proposed method is verified by evaluating it on a real-world typical urban ring road dataset. The experimental results show that 71% detected change segments are valid with only two round crowdsourced maps.

The dynamic response of fuel cell vehicle is greatly affected by the pressure of reactants. Besides, the pressure difference between anode and cathode will also cause mechanical damage to proton exchange membrane. For maintaining the relative stability of anode pressure, this study proposes a decentralized model predictive controller (DMPC) to control the anodic supply system composed of a feeding and returning ejector assembly. Considering the important influence of load current on the system, the piecewise linearization approach and state space with current-induced disturbance compensation are comparatively analyzed. Then, an innovative switching strategy is proposed to prevent frequent switching of the sub-model-based controllers and to ensure the most appropriate predictive model is applied. Finally, simulation results demonstrate the better stability and robustness of the proposed control schemes compared with the traditional proportion integration differentiation controller under the step load current, variable target and purge disturbance conditions. In particular, in the case of the DC bus load current of a fuel cell hybrid vehicle, the DMPC controller with current-induced disturbance compensation has better stability and target tracking performance with an average error of 0.15 kPa and root mean square error of 1.07 kPa.